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Santosh Kumar

Santosh Kumar is a senior marketing professional with extensive experience in helping business’ get the very best in BI and Analytics Solutions. If your business is looking to adopt and leverage analytics in any form, he is the person you need to be talking to. Linkedin , Twitter

A Tech Reboot: Why Are Fashion Companies Building Their In House Analytics Competencies?

Here’s why fashion businesses have moved from the traditional outsourcing model to building up their inhouse competencies:

Business Context: Unless you are the Kardashians, you understand your family better than an outsider. You know exactly what the business needs at a given time as opposed to a hired hand. When an activity is outsourced to an ‘expert’ outside the organization, they tend to make decisions without taking the business context into account. For example, a particular store might have lower sales because it had a new store manager and all the employees at that store are not aligned with his/ her objectives and practices. But an outsider looks through various forecasting tools and systems and has decided that a particular line of products, which would otherwise perform well, has to be discontinued.

Helps the business be more agile: Brand preference is no longer a thing. The choices are endless. And everything is needed here and now. But consider a business that has their decisions outsourced. Predictions for the season come in on Day 1. Orders are placed, and the production begins. When the products are displayed on Day 30, they realize that Cyan is in vogue and will be sold out by Day 35. A decision needs to be made on the spot by the store manager. But corporate has centralized operations to a team outside the country. Day 40, the store has completely sold out Cyan and is turning back customers. Day 75, Cyan is no longer in vogue, but the store is filled with Cyan pop-ups and unsold merchandise.

Quicker access to Data: The tech stack is complex, and data resides in multiple silos. The company has invested in so many tools and systems and has hired contractors from those providers as experts. To pull out a simple daily dashboard and present it in a format that is relevant to every store is a massive exercise. Even worse when every store manager wants the insights to be presented the way THEY want it. Compare this to an in-house team that has a system that unifies data from all the different systems and empowers business users to build their own dashboards, the way THEY want it, and you’ll see why outsourcing is not in fashion anymore.

Lower TCO over time: Fashion houses have long been hiring external experts, consultants, and hands to execute campaigns. But there is a problem. The problem of volumes. Executing multiple campaigns is turning out to be an expensive affair. As a shortcut, there are services offered by multiple consultancies and outsourcing companies that allow business to do ‘bare minimum’ marketing. Who bears the brunt of it? The customer. While agencies focus on each customer engagement as an activity, they tend to ignore terms like segmentation and personalization. Corporate CHQ might have contracted them to design fancy looking newsletters or trigger based SMS messages that go out in a set frequency and the customer is pounded with unrelated messages that result in churn at the end of the day. More and more fashion businesses are realizing this and are bearing the upfront cost of analytics software that helps them achieve personalization at scale. And they are now seeing the ROI with more customers engaging with the messaging and increasing spend with specific brands.

Bringing in a data-driven culture – In the world of Amazon, the fashion business is no longer just an art. A business that ignores the science part, is no longer relevant. Data-driven marketing has transformed from an innovative approach to a fundamental part of fashion marketing. Strategies are now built on insights pulled from the analysis of big data, collected through consumer interactions and engagements, to form predictions about future behaviors. This involves understanding the data you already have, the data you can get, and how to organize, analyze, and apply that data to better marketing efforts. Crafting experiences and engagements based on data has now moved to the boardroom and with access to real-time data and insights, even to the store.

For a more detailed view of what some of the leading fashion retailers are doing with their in-house analytics teams, visit our website

5 ways Fashion businesses can increase traffic – online and in-store

5 Ways Fashion Businesses Can Increase Traffic – Online And In Store

It’s 2018. What’s the most revolutionary development in fashion over the past few years? Is it the check and plaid mania? Or maybe the shirtdress or the oversized, lightweight dress? Maybe it’s the apron and baby doll dresses that have entered the summer wardrobe (from the Halloween wardrobe)? The real answer to that question is data or much rather, the explosion of data that fashion houses are exposed to and the millions of ways that data can be interpreted and acted upon over the last few years. Shoppers these days are connected all the time, mobile ubiquity provides them the freedom to browse, research and shop where they want.  You no longer need a customer to fill out a survey post their purchase, at a store, to understand who they are or what they like. You are now able to effectively build a profile of a customer using third party data sources like Instagram likes, for example, and get a pretty good understanding of their lifestyle before marketing to them.

If you looked at the fashion landscape and who’s really making the moolah, names like Amazon crops up. This is not right? They are not blue blood fashion retailer? Yet they are able to sell more fashion products than almost 40% of fashion retailers put together? They don’t have Giorgio Armani or a Coco Chanel suggesting what is the right outfit for every single customer? They don’t have a celebrity makeover team that is suggesting what color top a mother needs to wear to a soccer game? What they are good at is collecting data, deriving insights out of that data and acting on that data by putting the right product in front of the customer, at the right time, on the right channel.

You would assume that in this day and age, with all the power that data brings, increasing traffic to the store and getting customers to buy online should be fairly simple. Not really. The top challenges that fashion retailers are facing today when it comes increasing traffic are similar across the board and can be categorized into four main buckets:

  • They don’t understand the customer and what interests them. They are simply unable to the catch signals online and off.
  • Their customers don’t get context-aware, personalized communications.
  • They are not giving enough reasons for the customer to keep coming back. Their loyalty campaigns are limited to a quarterly newsletter blast for example.
  • They are unable to identify when a customer is ready to buy and lure them with offers that might attract that specific microsegment of customers.

To overcome these challenges the basic investment that’s needed for a fashion business is a Customer Data Platform (CDP) that gathers information from various sources like POS Systems, Online activity, Loyalty programs, CRM systems, third party data sources, etc. Once you have that in place, here are 5 ways you can exploit the insights and predictions to increase traffic to your store (online and off).

  1. Customer Life-cycle Marketing – Get the basics sorted. Create segments that you can use to put a label on every one of your current/ past customers. This could be based on where the customer is in their relationship with the brand, for example: just acquired, first-purchase, repeat buyer, loyal, wavering, churn.
  2. Micro-segmentation – Basic segments alone are not enough in the age of data. You need effective customer micro-segmentation based on lifestyle, life stage, behavioral, demographic and campaign responses to have an accurate view and understanding of each cluster. This would help you create highly relevant lists to run specific campaigns. There are some strong products out there that can help you do this using propensity modeling. You can forecast the future value of customer, rank and prioritize the customers and even allocate marketing budget using these advanced technologies. Make use of them.
  3. Effectively recommend the next-best offer – You can do this based on affinity between categories, brands and customer segments. For example, if over 40% of 30+ women bought a silver color sunglass with white oversized shirts, that’s a good indication for the system to throw up that insight to you or automatically recommend the silver glasses as a ‘complete the look’ recommendation online. If you can get a software that can identify cross-sell opportunities, personalize content/apps based on stage automatically using AI, even better.
  4. Path-to-purchase – NPS (Net Promoter Score) is not just a fancy word thrown around in the boardroom. It really does make sense, in every business. Identifying every customer segment’s journey/ path to purchase and chalking out all the touchpoints that led to a purchase is a key element of increasing traffic. Not only can it help you shorten the buyer’s journey but can help optimize marketing spend as well. Win-Win.
  5. Churn management – The last hat tip is to effectively manage the customers that are about to or have churned. The lazy approach, taken by most fashion businesses, unfortunately, is to include them in a newsletter group that regularly gets updates on a sale or some branding comm. At this stage, the business has effectively given up on these customers in my opinion. The first step to effective churn management is to identify customers that are likely to churn based on visit history, purchase/ lack of, campaign response and overall engagement. You can then build rules and logic to entice them with offers and promotions that are most relevant to their micro-segment, and in today’s world, these can be done automatically by the machine!

Advanced Analytics in The Restaurant Industry

The fight over which restaurant gets to satisfy your appetite is surely heating up. With ~$800B sales last year, this is an exciting space to watch, especially now. Customer expectations are at an all-time high and the consumer behavior is simply changing. Before we jump in, let’s look at what makes the restaurant business so unique.

Peculiarities of the Quick Service Restaurant Industry:

The average ticket value is low: Think about it this way, if you picked up all the bills from the last 10 times you visited a pizza chain and added it up, you probably won’t go beyond $1000. Juxtapose this with an apparel retailer and you would see that their average ticket value by itself is probably more than $1000.

Visit frequency is higher and cyclical: Compared to other retailers, the restaurant industry might see the same customers 8-10 times in a period of 6 months and before the frequency drops. Guest frequency is one of the most important metrics that a restaurant analytics tracks.

Size of the meal matters:  Apart from guest frequency, the moolah is made by maximizing the size of the order. A large coke with the burger? Small fries to go with that? You can try combo 1 and get an extra burger by paying $2?

Customer tastes don’t vary much: Fashion trends may change with time, but a guy who likes pizzas, likes pizzas, for a long time. (Until he signs up for personal training at the local gym at least). A BBQ enthusiast might always order the same slow-cooked pork roast sandwich as opposed to a vegan who might order a vegan sandwich, at the very same outlet. And this taste does not change much over time.

The restaurant industry is unique for sure, but if you look closer, the mandates from their C level can be boiled down to three simple goals:

  • Increase Meal Size
  • Increase Guest Frequency
  • Decrease Customer Lapsation

Gone are the days when oversized chicken costumes and cigarette smoking men, dressed as clowns were deployed as strategies to attain these goals. In today’s day and age, there are advanced tools, smarter analytics, and intelligible information that are helping restauranteurs devise effective strategies. With an increasingly digital-savvy customer, in a multichannel ordering environment, there is no dearth of data that is available for restaurants. The real trick is, however, to make sense of all these nuggets of information and derive insights that positively impact the Net Promoter Score (NPS).

Here are some examples of how some leading restaurant chains have put their data to work:

Identified Taste affinity clusters – A large pizza chain recently used millions of data points to arrive at 10 primary segments of customers and looked at their past purchase behavior to identify taste preferences. They identified that the large pizzas were primarily ordered by dine-in, family segment as opposed to the regular size pizzas which were typically ordered by office goers/ singles who visited the store. They even identified the times at which a certain pizza gets sold more and identified cross-sell items based on purchase behavior/ taste of a segment.

Buying behavior analysis– A burger chain took a different approach. They looked at purchase behavior across different channels to identify which menu items can be added to the combo for someone who orders through a mobile device as opposed to someone who prefers to visit the restaurant. They even used advanced analytics to get a single view of the customer by integrating their POS, mobile, web and social data to identify the customer and ensured that their messaging was consistent across all channels.

NPS and Feedback Analysis – A Chinese food chain used advanced analytics to integrate all the channels that they received feedback in (mobile, at location, social) to get a single view of the customer and layered it up with sentiment analysis. They used this data to give each customer a lapsation score which was then used to target them with unique offers depending on their lifecycle.

Store location analysis – Restaurant predictive analytics models were used by a coffee chain to identify the probability of a new store succeeding in a specific location as opposed to a location down the street. They identified pockets of demand and the model prescribed a set of potential locations in a given geographic area. They then used this data to score and rank comparable locations to determine the best location and format for the new store.

With the onset of advanced analytics in restaurant industry, the real question now is: As a customer, do they know what you are going to eat before you do?

The Unglamorous Truth of Fashion Retailing in 2018

The global fashion retailing market is valued at 3 trillion dollars, 3,000 billion, and accounts for 2 percent of the world’s Gross Domestic Product (GDP). In recent years, increasing vertical integration and the relentless rise of online sales have created fundamental structural shifts in the fashion retail industry. Classical players are now realizing the potential that e-commerce brings to their business and predominantly e-com players are looking at newer ways of fulfilling customer needs.  Read Prime Wardrobe. Economically, we see several trends shaping the industry, including fashion’s response to intensifying volatility, continued challenges with production hubs like China/Philippines, and the rise of urban centers. But the tectonic shift that the industry has seen in the past decade is the shift in consumer behavior. Traditionally, fashion retailers, be it high end or mass market, have relied on their products to ‘sell themselves’ and to a large extent, have relied on ATL advertising and seasonal sales to bring in the moolah. With an overcrowded market that is craving for the customer’s attention, do you think this model will sustain? There are several players that are looking at newer, smarter ways of engaging customers. Be it virtual fitting rooms or virtual stores even, VR is definitely making its presence felt. With the forecast for 900M AR-enabled smartphones by the end of 2018, the fashion retail industry is forced to rethink how it creates, showcases and retails its products. Oh, the glamour of fashion shopping! Current market analysis, however, suggests that almost 95% of fashion retailers are looking at solving existing challenges and are bullish on solutions that help them solve existential problems and are bearish on AR and VR. These ‘existential problems’ can be broadly classified as:
  • Carrying customer centric merchandise based on tighter forecasts, accurate merchandise plans, brand preferences, and shopper + location specific demand.
  • Fixing inventory challenges through real-time inventory visibility, effective inter-store transfers, MBQ resetting for greater sales and inventory efficiency.
  • Automating pricing and promotion decisions optimal price-offs that meet sales targets, maintains category profitability and predicts change in demand due to price interventions.
  • Effectively engaging the omnichannel customer that is influenced by trends online, prefers to read reviews, walks in store but looks for information on the mobile, wants offers, discount notifications, and communications to be personalized and makes a purchase decision anywhere and at any time.
Retail pundits have long been crying wolf about the convenience that online retailing giants are bringing to the fashion retail industry and the threat that comes with it. Well, the wolf is finally here. And it’s massive.

‘Moneyball’ing Data – A Closer Look at How Churn Models and Propensity Work

No matter how well you understand the subject and all the formulas, equations and algorithms that go with it, having the ability to predict something that is going to happen in the future is cool! If I had to make a pop culture reference to how someone can really control outcomes by making changes based on data, I would quote Moneyball. The movie that really made sabermetrics a household name and showed audiences how the ‘back office players’ really change the result of the game. Traditionally, a scout would assess which player has the potential to positively change the outcome of a game based on experience and ‘gut feel’. More scouts had failed at their job than the good ones that made it (with a whole lot of lucky guessing). The movie showed that if you took the human factor out of the play and looked at facts based on solid data, algorithms can make better predictions on who should be playing the next game. As machine learning, deep learning, artificial intelligence, etc. become mainstream words that are taught in primary schools these days, it pays to fully understand how the system truly makes predictions and prescribes actions that a business should take. In this article, let’s look at how churn models and propensity to buy models can help you ‘moneyball’ your data. First things first, to ‘moneyball’ your data, you first need to have data. It can be anything from sales data, customer demographics, visits, social profiles, customer feedback, etc. This data forms the basis for your models to get trained on is called ‘training data’. Models and algorithms are either pre-built or can be customized for a specific use case. For example, if you want to understand which segment of customers are going to churn in the next quarter, you can build a churn model which denotes the probability of a specific customer/ set of customers as a percentage. You can then get an output along the lines of: ‘Top 100 customers that going to churn in Q2 18’ and use that report to engage the customer better.

How does the model arrive at this percentage?

Much like how Darwin looked at the animals in Galapagos island to understand evolution, the model looks at the ‘training data’ to understand underlying patterns in past interactions. For example, the model could look at no. of calls made to the call center, emails sent to the helpdesk, ratings posted about products and drop in visit frequency to the site or store in the past 3 months and understand that every time a customer does all four, it is most likely that the customer is going to stop buying from you. This then gets converted into a percentage and let’s say that a specific customer did just 3 of those as opposed to all four, he gets a probability of churn % as 75%. Advanced models can also allow you to apply weight to specific attributes, like: A person who gave a low rating to a product gets a heavier weight as opposed to someone who did not visit the store in x amount of time. This weight can get you closer to predicting the likelihood of customer churn analysis. The more attributes you are adding to the churn model, the better. So how does a propensity to buy model work? Similar to the churn model, it looks at past behavior, attributes, demographics, sales data, etc. of the best customers in your training data that you want more of. For example, there is a set of thousand customers that are your real cash cows and spend $1000+ on your merchandise every month. This becomes the protagonist that you are going to refer to and compare the rest of your training data set with. Let’s say that one of the patterns that the model detected was that majority of the customers that bought $1k+ merchandise were loyal to one specific brand in your store. This purchasing pattern becomes a base for you to start marketing to others that have bought that specific brand but are in the $700 per month bucket. (What do you market to them? Look at the basket of the $1k+). This is just one example. Propensity models can slice and dice your data to look at attributes, behavior, and patterns that might be so counterintuitive that a human can never see a connection between them. The advent of AI and machine learning has really skyrocketed the applications of churn predictive models. Models have evolved from merely testing a hypothesis to systems that can offer prescriptions and take corrective actions. Several mounting ingredients promise to spread prediction even more pervasively: bigger data, better computers, wider familiarity, and advancing science. But the biggest evolution that churn predictive models are seeing is its democratization. Business users no longer need to depend on data scientists, analysts and IT to assist in decision making. They can now use and deploy these models with analytics providers making it super easy to drag and drop segments and ask pointed questions. You no longer need to understand the subject and all the formulas, equations and algorithms that go with it. With the help of churn prediction model, the customer churn analysis can be done by everyone and everyone can now do the customer churn prediction to know what is going to happen in the future. And that’s pretty cool!

3 Tips to Personalize Retail Newsletters

The power of sophisticated technology is no longer the privilege of technologists alone. It has moved into the hands of the common man. This consumerization of technology has put the customer in the driver’s seat and retailers are expected to deliver more convenience, more consistency, more collaboration and more customization than ever before.  This evolution is forcing the once predictable retail industry, to make the customer as the focus of every merchandise, marketing, store and supply chain decisions. As the industry shifts focus from pushing profitable products and goods to catering to the digitally empowered customer, customer engagement is playing a crucial role in bringing in the moolah. Today’s retailers have many channels, locations and devices to converse with their customers. Be it building a platform that supports omnichannel communication, installing indoor positioning systems that communicate in-store, empowering store associates with insights for effective clienteling, retailers are leaving no stones unturned. But what’s the low hanging fruit that every retailer HAS to get right? Email newsletters. Email newsletters are a great way to continue the conversation post a purchase, pave the way for further sales and keep your brand on top of the customer’s mind. This continued engagement has been a staple for any business and the difference between a great newsletter and one that moves into spam is, drumrolls, ‘personalization’. Email personalization has been around for a while now and there are some rudimentary tick marks that any basic CRM can offer you. Things like: adding the first name of the customer after a hello, scheduling emails to go out at times that the user has opened emails in the past, setting up a flowchart of ‘if this then that’ messages, etc. But to drive maximum outcomes out of newsletters one must look at the customer as an individual and fully understand who s/he is as a person before marketing to them. Agreed, doing this at scale might be a challenge, but that’s what segmentation is for. The more time you spend in segmenting and micro-segmenting your target audience, the better the results of your newsletters. Let me elaborate with these three examples/ tips that you can use to personalize your newsletters. Understand that your customer data is dynamic and evolves over time – The database to send newsletters either comes through the loyalty card/ membership data the retailer already has or acquires a ‘fresh’ database from a service provider that charges anywhere from 5c to $10 for each email id. Majority of promotions are run on these email id’s that the retailer has acquired over a period of time. If your team regularly cleanses this data, you are solving the issues of bounce rates, open rates etc. but the click rates? Click rates require you to offer up the right recommendation to the individual and the individual has to receive the most contextual offer that matches his lifestyle at that very point in time. For example, your loyalty data says that Mr. X bought $50 worth of meal kit on Jan 2016. This data has entered into your system and has been lying there for more than two years. Now your marketing team is looking to promote a newly launched, ‘fit and fab’ meal kit and Mr. X unwittingly enters into the newsletter list. But hey, he never clicked on the offer you sent him. Do we need to cleanse the DB again!!? What really happened is that Mr. X got married to Ms. Y, in Jan 2017 and started shopping for grocery items instead of meal kits. He also increased his share of wallet to $500 per month by shopping for ration supplies for the entire week. He drops into the store every Sunday evening to pick up stuff for the rest of the week. It’s all there in your loyalty data. Now, as a retailer who wants to personalize newsletters, would you look at individual purchase items or look at buying patterns and understand the intuitive meaning that every basket has? Micro-segmentation of your database just solves one part of the puzzle, but to really personalize offerings, you need to devise strategies and have systems in place to allow movement of contacts within different segments upon every new purchase. Your system should be able to tell you that Mr. X did not respond to the meal kit offer you sent him, remove him from the DB, look at his shopping patterns and recommend that you send him a ‘20% off on Organic tomatoes’ coupon. If you have your marketing team aligned with this thought, then its bound to increase conversions. But if you have systems that have recommendation algorithms that can do this at scale, every single time, even better! Look at ‘like to like behavior’ of similar individuals – If you are going to feed a stray dog, are you going to give him broccoli or left-over meat? The meat of course. Now how do you know that the stray would prefer meat as opposed to broccoli? Have you observed its eating habits over a period of time? Of course not. You have a dog at home that would give you a cold stare if you fed him anything other than meat. You have learned that if my dog would not prefer broccoli, then the stray would not either. It’s a logical conclusion that you arrived at by assuming that dogs, in general, prefer meat because my dog eats meat. Extrapolate this thought to your marketing. (Ah, the things your dog teaches you!). You have 10 items in your new product catalog that you have to market in your newsletter. Let’s take 3 different segments. Segment 1 – bought dairy and eggs. Segment 2 – bought meat, wine, and beer. Segment 3 – bought diary, eggs and chocolate sauce. Now, if one of the 10 items you were to market was a chocolate sauce, which segment would you pick? Segment 3. But based on purchase data, the baskets of segment 1 and segment 3 is almost the same. Can we try offering up a discount on the chocolate sauce for segment 1 based on the sales volume that segment 3 is seeing? Let’s label segment 3 as ‘Bakers’ and segment 3 as ‘suspected bakers’. See what happened there? You have created a microsegment based on purchase patterns and created a cohort called as ‘suspected bakers’. People can move in and out of this mailing list depending upon whether they purchase the chocolate sauce or not. Once you deploy such strategies to your database, you are effectively moving away from ‘trial and error’ marketing which cannot hold a candle to personalized marketing. Pop quiz – which segment would you categorize as ‘weekend barbeque’? Many messages, same database – Yes, audience exhaustion is real. The more the customer opens your emails and finds out that there is nothing interesting in there, the more your response rates and ultimately sales, suffer. It is tempting to send an exciting new product that you are launching in the store, but don’t give in! The same customer is present in your ‘bakers’, ‘suspected bakers’ and your ‘weekend barbeque’ lists. Are you going to send the same, ‘new product discount on ketchup’ three times? Surely, touch monitoring systems will detect send frequencies and alert you about potential overlaps, but they just look at time stamps against a particular id and do not recommend what message you need to send them. This is where your team needs to do the due diligence and spend considerable time effort in creating microsegments. And stick to it. If you have identified ‘weekend barbeque’ as a cohort, you must stick to messaging that is appealing to this cohort. This messaging can be around the lifestyle that a weekend barbeque enthusiast might lead. For example, you send him recipes for killer BBQ ribs in week 1, discount on BBQ sauces on week 3, invite for a store cookout competition on week 5. If the contact does not respond to any of these, then it’s time to move him out of the cohort. But imagine the loyalty that this sort of message is bound to bring (and the sales). You could also try segmenting based on the specific shopping behavior like ‘most likely to respond to coupons’, ‘most likely to respond to try new products’, but until you get to segmenting customers as ‘most likely to respond to a coupon on organic eggs’, you are not acing personalization.

Food and Grocery Analytics in 2018 – The New Age Realities

Food and Grocery Retail is one of the fastest growing segments and as per an estimate, by 2022 consumers could be spending $100 Billion on online grocery alone, constituting more than 20% of the overall market, worth almost $ 1.5 trillion. While there is a dip in disposable income of an average citizen, the daily bread and meat is not something she can cut down on. Retailers must build their strategy keeping her in focus, giving her the seamless and intuitive shopping experience, she desires. This is in line with what Gartner suggests, it is vital to reorganize merchandising and marketing around the needs of the consumer. But it is easier said than done. According to a study by FMI and Nielsen – many retailers are not prepared to meet the needs of omnichannel food shoppers, not for lack of multichannel assets or touchpoints, but for want of cohesive strategy integrating the physical stores and online business processes. They are struggling with fragmented data and are “failing to adequately share shopper data, segmentation and other consumer insights leading to missed opportunities”, the report claims. Poor forecasting ability also hinders scalability for the retailers. Most retailers find it challenging to strike a balance between demand planning and response execution and fail to reach the right customer at the right time with the right offer. Their marketing promotions are still following the traditional product-centric strategies rather than being aligned to customer choice and preference. The new age realities are making the hitherto followed strategies and processes redundant. In today’s world, the shopper is being lured with sophisticated technologies that not only recognize them as individuals, pre-empt their shopping needs, but also help them plan their shopping and make most of the prevailing offers. How do the average Food and Grocery retailers keep pace with such sophistication? What should they do if they have to survive and thrive in the omnichannel world and make their storefront an attraction for the customer to pay repeat visits? How can they come closer to the customer and re-imagine their entire business by keeping the customer in the focus – be it merchandise, inventory, store operations or marketing? Further, how can they empower the teams on the shop floor with insights and prescriptive actions to deliver unique experiences to the customer and ensure the suppliers are always replenishing the right shelves with the right assortment? Here is some essential and effective retail analytics solution for food industry which retailers can adopt in building the F&G store for the connected shopper by reimagining the systems for store operations, supplier collaboration, trade promotions, and marketing campaigns. Download Whitepaper

From Analog to Algorithms – The Evolution Marketing in Retail

As someone who loves retail and the technology behind it, looking back at how things were done in the past, really lets me appreciate how far we have come. Consumers no longer see a distinction between online and offline shopping. Whether it’s searching on a laptop, browsing main street shops or hanging out at the mall — it’s all shopping. This evolution of shopping behavior has brought some drastic changes in the way sales and marketing operations in a retail set up. The one on one relationship that a corner store thrived on has translated into a different kind of conversation, a conversation with machines. I am not talking about some sci-fi, ‘Ex-Machina’ kind of stuff, but how machines, more specifically the algorithms within those machines are enabling marketers have 1 to 1 conversations: at scale. Imagine you walk into a party and have the ability say a hi, ask a how are you, and have an individual conversation with a hundred, different people at the party: at the same time. That’s superpower is what makes today’s retail fascinating. Roles within the retail organization have had to adopt and adapt to newer ways of working, and none more so than the roles within marketing. You know, the ones who generate the demand, increase footfalls and visits, engage in conversations, influence a sale, increase basket value, promote loyalty. Those guys. The way they are engaging the customer and enhancing the customer experience has truly gone through a massive overhaul. And the true beneficiary of all this advancement, the customer. Allow me to elaborate.

3 ways marketing in retail has evolved.

Deciding on what promotions to run and how – Grown men dressed as cartoons, people handing out free samples and pamphlets, huge banners offering even bigger discounts – all calling attention to the new cereal brand that a CPG manufacturer launched. Scenes that today’s Marty McFly would see if he went back to the 80s and 90s. Real estate costs have ensured that retailers can no longer afford to have huge promotional activities in-store, but there is a much bigger space out there that offers unlimited space to run promotions, online. Stores now have apps, websites and an ever-increasing following on social media. These are perfect playgrounds for the marketer of today to announce promotions on new product launches, discounts, and offers. But before one gets there, they must decide what promotions to run. Analytics solutions provide insight into consumer behavior and patterns that allow marketers to decide on a specific segment of their marketable audience a promotion might really work on. Add demand forecasting based on local and seasonal variables to the mix and you know have a better understanding of which offer needs to be promoted. The big players are even accounting for the demand fluctuations based on the weather to ascertain what products need to be promoted. Isn’t that next level stuff? Digitization of conversations – One salesman for every two aisles. Every customer that lingers on for more than 5 mins gets a ‘hi, how may I help’ you. Great playbook, worked well. Then. Today’s shopper learns about your product, reads reviews for opinions, gets influenced by peers and knows what needs to be bought before they even enter the store. Face to face conversations is no longer the moment of truth. This would mean the overall marketable audience has increased exponentially and the conversations you need to have with every prospect plays a crucial role in increasing awareness and interest in a specific item. Several tools are now available in the market that let you push mass communications on social, email, mobile and even in-store displays. But the real trick is to stitch a flow that makes it seem like every touchpoint with the customer is crafted per the individual’s lifestyle and appears personalized. This is mainly done by algorithms and the approaches can be broadly categorized into two types. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user’s preferences. Keywords are used to describe the items and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past (or is examining in the present). Without these algorithms helping them communicate digitally, a retail marketer today would face a tremendous challenge in having a ‘voice in the market’ and keeping their brand ‘top of mind’. Omnichannel, real-time communication – The consumer of today no longer sees online and physical stores as two different entities. If your brand must drive loyalty, it must provide a seamless experience online and off. Click and collect, same day delivery and other fulfillment models sure help in the delivery of your products but the bigger challenge is to communicate with your prospect effectively in-store, around the store and online. A marketer now can get a single view of the customer across all the touchpoints and the information no longer resides in silos. Remember the days of last touch attribution? Location-based analytics can now tell the marketer when the user is near the store and a targeted promotion can be sent based on the users online browsing behavior. In store beaconing technologies have given a serious boost to a marketer’s ability to send relevant offer promos within the store. Old school retailers used to rely on pop up displays, VR and off late, AR too. But consumer preferences clearly indicated that these technologies are not the best usage of time, space and more importantly, money. ‘Tell me if you have what I want, tell me if it can be bought cheaper, and tell me where and how to buy it’ is all the information that today’s consumer needs and the retailers are delivering. AI and prescriptive analytics have further augmented their ability to communicate in a complex omnichannel setting by automating time-consuming tasks like content creation and optimizing spend. The old school, Don Draper sort of marketer is sure learning how to use tech, and it’s not just the marketer that is benefitting from this evolution, but the customer too. If you are looking for a solution provider that has enabled multiple retailers with the above capabilities, take a look at

4-point plan to launch or optimize a private label

Intense competition in the retail space has ensured that the customers are spoiled for choices and as a direct result, margins for the retailer has dropped considerably. Imagine a customer walks into the store looking ketchup and sees about 40 assorted brands on the shelves. S/He is most likely to pick the one that has the maximum brand recall, or look for something that is the cheapest or, randomly picks one and drops it in the cart – it’s just pureed, processed tomato pulp after all. Majority of shoppers can be categorized into these three buckets: Loyalist, discount shopper or the impulse shopper and if a retailer is keen on maximizing profits from these profiles, cutting the cost of procuring end products from manufacturers and middlemen would certainly help.

Enter the protagonist: Private Labels

According to a recent study by IRI, private label brand significantly outpaced the industry average growth rate (4.1% to 2.8%). Private label CPG now accounts for over 17% of multi-outlet unit sales and enjoys a nearly 22% unit share in the grocery channel. Seven out of 10 millennials prefer stores that have a wide selection of private label products, and nearly as many (66 percent) often buy private label options over name brands. The appeal isn’t limited to younger shoppers; consumers from all generations view private label as a way to save money and improve value without sacrificing quality.

If you are a retailer and looking to launch your own private label/ optimize profits of your existing label, here’s a 4-point plan to get you kickstarted:

Identify unmet customer needs – No demand, no need to supply. To identify the demand, there are several routes that one can take:

  • Feedback from store operations on specific items that customers keep enquiring about frequently
  • That specific item that keeps going out of stock no matter how much of it you stock
  • Do a market basket analysis of all the items purchased over the last few years to identify maximum SKUs sold
  • Analyze search trends and patterns on your website and app
  • Feedback and survey analysis of customer sentiments
  • Look at competitor labels/ talk to analysts

The foundation for identifying customer needs is, of course, the availability of data and a strong analytics platform that can help you gain insights out of that data. Once you have your analytics in place and reviewed the above items, you have a clear idea about the product line that can be the guinea pig for your private label.

Put your supply chain to work: Traditional supply chain looks at optimizing the supply chain and includes:

  • liaising with suppliers to eliminate bottlenecks
  • sourcing strategically to strike a balance between lowest material cost and transportation
  • implementing just-in-time techniques to optimize manufacturing flow
  • maintaining the right mix and location of factories and warehouses to serve specific stores
  • using location allocation, vehicle routing analysis, dynamic programming, and traditional logistics optimization to maximize the efficiency of distribution.

Can you imagine the amount of information that is available with all these ‘external employees’ and your internal teams?

Private label means relationships with industrial and product designers, materials suppliers, factories around the world and logistics providers. And all these ‘new’ relationships are within your arm’s reach. Factor in the analysis you did about customer preferences and you can narrow down on the specifics. For example, with the ketchup example, you learned that the loyalists prefer fresh, organic, no sugar ketchup. Conceptualize the product with your design, category managers, and product teams. Get your team to reach out to factories within a 200-mile radius and link them to organic farmers within the area. Identify optimal routes and logistics to the factory and distribution from factory to stores. Put a production and supply schedule in place based on demand and sales forecasts.

Get the pricing right –  Is your target audience a discount shopper or impulse buyer? What are the prices of other products that you are placing right next to this one? What are competitors pricing their private label of similar offerings? There are some top-notch retail solutions out there that aid retailers price your product intelligently by analyzing a variety of factors that impact pricing such as the age of stock, lifecycle stage, inventory count, sell through, demand, competitor price analysis, promotions or bundling. Given that the lifecycle of the product is at a nascent stage, you can, however, take some liberties and take additional factors into consideration further down the lane.

Take your overall costs for that product into consideration, and you’d also want to get a general idea of how that product category is priced. In general, private label products are priced lower than national/ international labels. This is because private label products lack the brand value carried by bigger brands. These brands have earned a reputation and consumers will pay top dollar for that brand value. Private label products have long carried a reputation for being a “cheap” alternative and therefore have weaker brand value.

The key to getting the final price right is to simply let the product present itself to the customer for a set period of time with alternative prices. You can then use algorithms to recommend the optimal price.

Decide Optimal Branded-Private Label Mix: You must get your category managers involved in this biggie. Category Managers can continually optimize the product mix by recommending products to be dropped, retained, replaced or substituted. They can also identify complementary products that can be bundled based on purchase patterns, linkages and cross-sell opportunities.

The other decision you could make is whether to go with an instore heavy/ web/app heavy placement of your product. Capturing web search data will help retailers make that decision — Are customers searching for a brand or features in a specific category on the site?

Retailers must realize though, that adding additional products into an assortment will eventually lead to cannibalization. While you are heavily invested in making your private label work for you, also pay close attention to which brands are getting affected in return and if it’s worth having the other brand on your shelf.

I hope these pointers give you a good head-start in getting your private label strategy right. In summation, consumers are giving retailers permission to grow their private label brands. The real question is will retailers grow private label brand value or simply grow more private labels? You really know that your private label strategy has worked when the discount and impulse shoppers have been converted into loyalists.

If you are looking for solution providers that can help you with your private label strategy, visit:

Counterpunching AmazonGo

Unless you are living under a rock, you have probably heard of AmazonGo. At the time of writing this article, there were 10M+ views of the video Amazon made to show the world how it’s done. There have been millions of shares on Facebook and Linkedin, and the retweets ensured almost half the world has already seen the ‘future of retail’. For the benefit of the ‘living under the rock’ cohort, here’s how it works:
  • All you need to get started is an Amazon account, the free Amazon Go app, and a recent-generation iPhone or Android phone. Once you have the AmazonGo app, scan to enter the store and the store recognizes who you are.
  • Once inside, there will be no store assistants apart from the people baking and making fresh food on counters.
  • WiFi or Bluetooth LE connections will ensure customer recognition at every aisle.
  • Proximity sensors and cameras recognize that you have picked up a specific item from the shelf and it is credited to card/cash in your AmazonGo app.
  • If you put the product back, the item’s cost is deleted from the AmazonGo app and money is debited
  • You only need to bag the items and exit the store. No checkout lines. Your Amazon account automatically gets charged for what you take out the door.
Jetsons? Scenes from the spaceship in Wall-E? While the tech is surprisingly refreshing, and the experience is novel, the store is currently available in only one location, Seattle. Business Insider reported they had seen internal Amazon documents that described Amazon opening up as many as 2,000 stores, over the next ten years. That’s still not enough stores to cater to every shopper across the civilized world. This gives enough time for the rest of the retail world to catch up and start applying themselves to enriching the shopping experience. So how does a retailer go about counter punching the AmazonGo phenomenon? Do you need a $10M a year budget to develop labs that can prototype ideas? It’s simpler than you think. Advancements in retail are within the reach of every retailer that is inclined to collect data. Technologies are readily available to stitch a customized, personalized experience to shoppers that would not travel miles to get to the only AmazonGo store in their city. See what I did there? Here are 5 ideas that can be deployed in supermarkets and convenience stores in less than 2 quarters: In-store shopper tracking – One of the biggest challenges of shopping at a store is not knowing where your product is shelved. An average shopper spends about 12 mins looking through one aisle to find a product. As a retailer, you might argue that this is time well spent, they get to see all the other products on display. But as a shopper, this is the kind of shoddy experience that AmazonGo feeds on. Indoor positioning systems are now available to identify a shopper when they walk in and beams a virtual map of the store on their mobile device. You can search for the product that you are interested in and the virtual map takes you there, kind of like Google directions. Digital personalization – While AmazonGo is still testing grounds, Amazon, the virtual store has years of experience in personalizing the digital experience. If you have taken the ‘reverse Amazon plunge’ and started selling online, personalizing your website, app and communications should be your top priority. Recommend best offers and products, recommend products bought together, hot selling products etc based on personas, browsing history and past purchases on your website. You can send geo-relevant offers, best offer set, shopping and wish list based offers through your app. Dynamically present offer and content on newsletters, through a recommendation engine, using customer attributes such as home-store and lifestyle. Customer-centric assortments – Imagine the amount of data crunch that is being done behind the scenes of AmazonGo to ensure that the right product is available when the customer walks in. They have to get it right, there is no one to talk to and ask for the availability anyway. Your Category Managers can now continually optimize the product mix by recommending products to be dropped, retained, replaced or substituted. They can also identify complementary products that can be bundled based on purchase patterns, linkages and cross-sell opportunities. Additionally, there are now solutions that can recommend appropriate price points for products that are slow movers based on external factors and internal data. Use all these data to ensure you are carrying the right assortments and you never have an out of stock event in your store, ever. Real-time inventory insights – This one’s a biggie. This by far is the single biggest challenge that daunts any retailer and should be the first bullet on your 2018 wish list. Solutions now provide real-time information of potential out of stock situations and trigger purchase orders for stock, all automated. The other ‘simple fix’ is to trigger automated intra-store transfers for stock replenishment. Couple this with a solution that recommends markdowns for excess inventory and streamlines the overall buying process, and you are already making millions, by cutting your losses. Democratizing insights to store managers through mobile apps – While walking through a store without any human interaction and picking up your item and leaving the store might seem exciting the first few times, I think what makes shopping in-store appealing, are the interactions themselves. The biggest challenge in getting the store managers and associates to deliver at the moment of truth is lack of data and insights about the customer. They need to know what is relevant to them at the right time and medium. Communicating complex insights and information through emails is passé. Insights need to be communicated to the store operations team, comprising solely of millennials these days, through their mobiles and apps. They need to have access on-the-go and not have to go to a laptop/ desktop placed at a part of the store with the least footfall. I hope these pointers give you a head start in counterpunching the giant. If you are looking for a solution provider that has enabled multiple retailers with the above capabilities, take a look at

Amazon vs Rest of the World

Amazon has 536 Million Products on sale as of now. Not SKUs, products. Let me put that in context. If Amazon wanted to, it can give one unique product to every single person in all of North America as a Christmas gift and still have the same products on its virtual shelf for the rest of the world to buy. Isn’t that mind-boggling? People are now searching for ‘stuff’ on Amazon directly because they know that Amazon’s endless aisles are sure to have the product they are looking for (This ‘searching on Amazon’ phenomenon has other players like Google worried, but more on that, in another article). How does a retailer, specifically the ones who deal with individual categories resist getting steamrolled by this Goliath? You can never beat Amazon in Assortment volume, but here are some actions you could take to challenge the Goliath.

Drive real-time engagement:

Treat every guest context such as restaurant visit, cart composition, promotion response, coupon burn, e-commerce clicks, etc. as a dynamic trigger for personalization. Intelligently and uniquely respond to every guest with next best offer based on past behavior, like-for-like patterns, and marketing priorities.

Smarter Category Management:

Localize assortments based on sales forecast, customer demographics, purchase patterns, weather data and seasonal trends.

Plan Assortments better:

Identify fast moving, slow moving, overstocked and understocked items to help identify the right assortment for the next season.

Optimize Markdowns:

Analyze historical category and brand performance, market trends to recognize markdown performance and leverage those insights to design effective markdown campaigns.

Democratize Store Insights:

Arm store managers, department managers and associates with necessary insights about sales, promotions, inventory and employee productivity.

Boost fulfillment performance:

Identify the bottlenecks in shipping delays and ensure better customer satisfaction by making smarter vendor selections.


Enable store associates to initiate conversations with high-value customers based on their personas, past purchases, and next best offer.

Win back Lapsed customers:

Identify guests who have lapsed or at risk of leaving and proactively take actions by identifying the right mix of offers and channel strategies to engage them. If you are interested in seeing some real-life examples of how other retailers are combating Amazon with innovative store experience ideas, Download this Whitepaper

6 ways AI is helping retailers in real life

When E-commerce took off the way it did, every retail pundit predicted the doom of in-store shopping as we knew it. The novelty and glamour associated with in-store shopping were replaced by convenience and never-ending aisles that an e-tailer brought. These ‘new guys’ were born and brought up in the tech industry and knew how to use data to make products more accessible to customers. But, things always don’t go as planned, do they? The hostile takeover of the retail kingdom is getting harder and as it stands today, the e-commerce players market share is still in single digits. The fightback has a lot to do with, surprise surprise, technology helping the physical stores. The laggards are learning and learning fast. If you look at the employee break up of the ‘new age store owner’, you will start seeing more analysts, data scientists, and digital innovation roles than the erstwhile, store help/ associates. This truly is the make or break period for the physical store owners and they are going all out in adopting modern technologies to counter the ‘born with data’ e-com frenemies.

So, who’s helping them?

Technology companies that offer both, technology services and products. They have created entire ‘retail-specific platforms’ to make the transition easy. For the novice store owner, something like a data visualization tool that offers colorful graphs might look really appealing and they go all out in acquiring an enterprise BI solution. But the more experienced and thoughtful ones are turning to much more advanced tech like AI, machine learning, and prescriptive analytics to truly operate at scale.

Today, AI is no longer a concept that has limited use-cases. The leading product companies are showing the way by applying AI in real life and retailers are gobbling it all up. Here are 6 ways retailers are using AI in their day to day operations:

  •           Stores are using AI to automate real-time store engagements through apps
  •           AI is now helping retailers by prescribing customer-centric assortments that are more likely to drive higher store sales in a specific location.
  •           Merchandisers and Category Managers are using AI to decide on price-offs and promotions for every SKU
  •           AI is also being used to recommend personalized, next best offers for individual customers
  •           Channel optimization is the latest of the lot. AI is being used to identify the right channel to target a specific customer
  •           Store managers are using AI to predict out of shelf and out of stock events to take corrective replenishment actions

If you are interested to learn get a demo of how all this is done, ask Manthan . Or better still, if you are visiting NRF 18, set up a meeting with the team here

The year 2018: Predictions for Convenience Stores

As the convenience stores segment goes through yet another year of evolution, catering to the new ‘I need everything personalized’ customer is clearly standing out as the biggest differentiator from the competition. To take it further, here are top three trends that are clearly standing out.
  • Convenience chains are increasingly turning to predictive and prescriptive analytics to make automated decisions that are based on data
  • Merchandisers and store managers are being empowered to make decisions on the go
  • Customers are expecting personalization and as a result, brands are doubling down on digital customer engagement
To maximize revenue impact and be prepared for 2018, C-Stores have 5 levers that they can use to be successful.
  • Have a system that can prescribe profitable and timely decisions for merchandising and store managers about assortment, inventory, pricing, promotions and day-to-day store operations
  • Empower marketers to identify revenue generation opportunities and predict customer behavior by understanding customer behavior and preferences
  • Improve customer engagement by executing machine learning based personalized promotions in real time across email, SMS, mobile app and social
  • Create a high performing agile supplier network through comprehensive insights, process automation, and data integration between the retailer and suppliers
  • Implement local pricing strategies across stores based on competitor pricing and customer segment needs
To know more about our Convenience Store Solution, Click here to Download

Manthan and Computer World Partnership in MEA – An interview

Samantha: We have been reading about the Manthan and Computer World Partnership in a lot of places, can you tell us what this partnership brings to the market? Ehtesham: Retailers in MEA need the right industry focused analytics solution to face the current business challenges and operate profitably. And key to this is the mining of insights within their data. Its appropriately said these days, that Data is the new Oil! Manthan Analytics solutions are purpose built, industry focused, aggressively priced and quick to deploy. And through our partnership with Computer World, we intend to leverage the extensive reach of our partner, to bring our award-winning solution set to the regional clients in Saudi and Bahrain. Samantha: Can you tell us about the work that Manthan has been doing in MEA in recent times and the opportunity you see here? Ehtesham: Manthan Middle East has seen a phenomenal growth recently of over 65% YoY in revenue, increased clientele count by over four folds and acquired like-minded partners in over 30 regional countries representing us. All this in the so called ‘challenging environment’. A lot of our existing customers are seeing tremendous value from the solutions we have delivered. I recently met with one of our top customers in the region and they wouldn’t stop thanking us for ‘putting their data to work’. I see a lot of retailers in the region still using a standard, run of the mill ‘excel-ytics’ or generic and basic BI solution that does not fit the need of a retail industry. They end up spending a lot more money on acquiring more tools and complementary solutions to make up for the lack of decision enabling insights. But all this can be accomplished with our retail specific solutions and we hope to bring this to MEA, in a big way. Samantha: Final words for a prospect that might be reading this? Ehtesham: Instead of investing in new transactional systems or complicated non-purpose built BI or Analytics applications, you should be leveraging the powerful insights within your own data, and do that quickly, to get/keep your businesses on track. Contact Ehtesham

Analytics Based Omnichannel Experience – “The Everything Store” that started its journey by selling books in 1995 and has been responsible for many physical book retailers closing down ever since has finally decided to be one itself and has started its first physical bookstore (which it calls “Amazon books”) in 2015 and today there are 10 stores. According to some reports, they are planning to open hundreds of such stores. But, wasn’t Amazon created to be an alternative to brick and mortar stores? Wasn’t Amazon the reason why E-Commerce has become what it is today? What’s Amazon doing with these stores?

The Omni Channel experience

Amazon is creating a shopping ecosystem that seamlessly spans the online and offline worlds. The two are also linked by Amazon’s $99 a year Prime loyalty program, which gives online customers, perks such as a video streaming service and shipping privileges and at Amazon’s bookstores, cheaper books. According to Customers who have visited these stores, inside, it looks like a Web page that has come to life. “Highly Rated: 4.8 Stars and above,” reads one shelf. Another highlights books “Read Around The Bay Area,” while a third features “Books with More Than 10,000 Reviews on”. Such a display ensures a consistent experience for the customer on all channels.

Leveraging their biggest Asset- “Data“ captures humongous amounts of data and is one company which makes full use of it; this is no different for the stores, some ways in which Amazon leverages data in the stores are
  • The store uses data from customer purchases, as well as nearly endless reviews, to decide which books to put in stores.
  • The shelves display positive reviews and star-ratings from the website
  • A popular feature in each of the stores is the recommendation section. In that area, Amazon displays hit books with multiple similar titles that maybe aren’t as well known
  • Massive amounts of data do give Amazon book pickers a nice perch from which they make their decisions. Take, for example, a section of the store labeled “Page Turners.” Those are books that have been read in three days or fewer by Kindle readers.

Optimum use of available space

“Amazon books” stores are typically smaller than the regular bookstores like “Barnes and Noble” but they use this space effectively. According to Amazon CFO Brian Olsavsky, the bookstores are meant to be much more than just a place to sell books. They’re also a great way to showcase Amazon’s hardware devices and drive up their sales. “We think bookstores, for instance, are a great way for customers to engage with our devices, to see them, touch and play with them, and become fans, so we see a lot of value in that”, he says. Amazon uses shelf space to display the covers of books facing outwards instead of spines; according to Amazon, the decision was made to showcase the authors and their work, rather than efficient use of space. Each book is displayed cover out, with customer ratings and reviews printed underneath.
If you are looking for ideas to go omnichannel, Manthan can help you. For some quick ideas, download this whitepaper that talks about 4 steps you can take to attain multi-channel nirvana: If you are interested in a 15 min. discovery call with Manthan, do write to us