Customer Analytics – Page 5

Riding the Rollercoaster: Your Approach to Customer Journey Marketing

According to the Harvard Business Review, it can cost anywhere between five to 25 times more to acquire a new customer than to retain an existing customer. Yet brands continue to spend an average of 88% of their budget for customer lifecycle marketing in awareness strategies.

Instead of driving new customers to the top of your funnel and encouraging them to make a purchase at the end, brands that understand and address the various stages of the customer lifecycle can achieve a greater impact.

For a fashion brand, this makes a lot of sense. A customer may become aware of your brand through a pop-up sale resulting in a $40 value to the brand. But if that customer continues to be engaged, and returns five times per year and with an average spend of $40 each time, then in five years, the customer is worth $1,000.

Customer Lifecycle Marketing for Retail Success

Understanding the progression of steps a customer goes through when considering, learning, purchasing, using, and maintaining loyalty to a brand is important to your retail success.

To learn how to craft the right strategies that will get a potential customer’s attention, turn her into a paying customer and nurture her to loyalty, download our paper, “Adapting Marketing to the Customer Lifecycle in Fashion Retail”

Why Customer Data Platform Is A Must Have In Retail Today?

With an array of options at their disposal, customers are becoming more and more demanding with each passing day. Expectations are high and if you cannot meet them, they will move on and find another brand to lavish their time and money on.

To put it simply, brands must pay more attention to its customers to deliver an experience the customer will want to return to, again and again. A thorough knowledge of the customer and his/ her preferences are therefore mandatory, as it has become essential to provide every customer with a very personalized experience.

The problem of isolated customer data

Personalization is daunting as customers today interact with brands through a variety of channels both online and offline. The collection of customer data is scattered in legacy systems – with departments such as sales, marketing and customer service holding information in silos. In addition, information contained in sources such as social media, mobile apps, email, SMS, POS, coupons, loyalty programs etc. add to the barrage of data.

Having all this data is pretty useless if the retailer is unable to connect it and utilize it to make customer-centric strategies and decisions.

Gaining a single view of your customers

Creating a unified view of the customer requires data cleansing, curation, resolution, and transformation. It is also imperative to provide seamless access of customer data to business users without requiring IT intervention.

Nothing is more important in retail today than a single view of the customer, irrespective of where she chooses to do business with your brand.

Consider a hiking enthusiast who buys shoes online and talks about how excited she is about the new shoes and her upcoming trip. She may then later visit the store to buy a jacket and nylon pants. She also redeems her loyalty points with a coalition partner brand to rent backpacking camera equipment.

The amount of information around this one customer alone is a complex amalgam of data. By factoring in more information (such as Instagram feed as to where she is hiking, her lifestyle), a clear, unified 360-degree view of the customer can help retailers and their partners know what products and content to promote to drive conversions.

Enter the Customer Data Platform: A business managed a system that brings together a company’s customer data from multiple departments and channels to enable customer data modeling and optimization of timing and nature of marketing messages.

How retail businesses benefit

Let’s look at some specifics of how a Customer Data Platform can help retailers get results:

Increased revenue from existing customers through personalized customer engagement and special offers based on individual preferences.
Improved customer satisfaction through improved customer focus – the better you know your customers, the better you’re able to anticipate and give them what they need.
Optimized ad expenditure by knowing exactly where a customer is in his purchase cycle, and intelligently targeting them with relevant messaging.

All of the above have a direct and immediate impact on both revenue and profitability of a company, making the need for customer data management real and urgent for retailers.

Data science is today a game changer in marketing, and the importance of having the right Customer Data Platform in place becomes even more apparent.

Manthan view of CDP

We define CDP as the core data infrastructure that can ingest, manage and serve data. Manthan Customer360 is a CDP that also houses analytical and data science capabilities such as segmentation, look-alike predictions, data exploration, micro-segmentation, and self-service analysis.

To learn more about how a CDP can serve your retail business beyond customer analytics – operations such as assortment planning, pricing etc., download our Customer Data Platforms handbook

Gartner’s 2018 Hype Cycle for Retail Technologies. What you need to know.

Gartner’s Hype Cycle for Retail Technologies 2018 is out, with trends for technology leaders. This year, the Hype Cycle has identified democratized AI as a key trend – products and solutions that “blur the lines between human and machine”.

We are pleased that Manthan has found mention in 5 categories in the 2018 Hype Cycle.

AI in Retail (Rating: Transformational)
Algorithmic Retailing (Transformational)
Cognitive Expert Advisors (High)
Customer-Centric Merchandising and Marketing (High)
Algorithmic Merchandise Optimization (High)

These ratings are a testament of invention and innovation in Artificial Intelligence, Advanced Analytics, and Cloud at Manthan. And a vision, to
create context-aware, AI-powered analytics products that bring the true
power of AI to every role in your business.

Manthan’s efforts have been focussed on bringing analytics-driven
decision-making to the real user. To design sophisticated analytics
products in a manner that everyone can use them. Which we believe, is
critical to the businesses dealing with the real-time, connected customer.

We architected our products by re-interpreting 4 critical areas –
Analytics Consumption, Algorithmic Processing, Solution Engineering
and Data Management.

1. Re-interpreting Analytics Consumption

Our primary goal, as Gartner puts it, is to “blur the lines between
human and machine”. This is what led to the creation of Maya – the
world’s first AI-powered conversational agent for business analytics.

With this, analytics becomes easy to consume. In a simple,
conversational format that remembers context and processes
information in real time, based on the user’s intent and his flow of
analysis. Maya is integrated with mobile, desktop and personal assistant
devices and can be invoked anytime. It offers both general and role-based
models for analytics consumers.

Maya makes use of machine learning, deep learning, advanced analytics,
cloud computing, natural language processing (NLP) and generation
(NLG), intent analysis and context-aware computing. But all you need to
do is ask.

2. Re-interpreting Algorithmic Processing

Today’s digital business is generating millions of data points, across
sources, every day. Taking traditional hierarchical approaches to
analyze data is just not physically viable. Your solution should be able
to conduct auto-discoveries, root-cause analyses, and auto-recommend
best outcomes, based on simulations.

Manthan’s analytics platform algorithmically processes anomalies,
outliers, and exceptions to recommend actions that can achieve clear,
smart goals.

3. Re-interpreting Solution Engineering

As analytics processing becomes complex, analytics experience needs to
become intuitive. Solutions need to be designed for the real decision
makers and should embed real business contexts.

Manthan’s solutions are designed to bring together advanced analytics
and algorithmic capabilities for specific use cases across retail. Our
solutions also come with the ability to scale and to incorporate new use
cases.

4. Re-interpreting Data Management

The digital business generates much more data than the traditional one,
with new data sources emerging all the time. While some of this data
drives repeatable use cases built around standard business processes,
you also cannot lose sight of new use cases that elevate the customer
experience.

Manthan offers a full-featured data management platform that can deliver
production-grade enterprise analytics in a governed model. But at the
same time, it also drives rapid experimentation and innovation with an
architecture that can ingest and mash new data sources at scale in a data
lake architecture. This supports on-demand data-processing, giving
businesses real-time decision-making abilities.

We have what you need.

We have re-interpreted analytics delivery with AI. And an elastic
cloud infrastructure and server-less computing capabilities provide the
necessary performance and agility you need from a new age data
a platform that can deliver real-time decision-making.

Tomorrow’s technology does not require a screen in front of you. Or you
in front of a screen. It will walk with you, whispering real-time
recommendations in your ear, based on intense, granular analysis.

That’s truly democratized AI. And that’s what we have for you, today.

14 Metrics that Impact Customer Churn in Fashion Retail

Fashion retailers have been seeing disruption to their businesses for over a decade. From digitalization and the “Amazon effect”, to subscription models and fast fashion, most fashion brands have had to operate at a cut-throat level of competition in order to just survive.

In this situation, brands are particularly impacted by the loss of a customer. Not only do they experience a direct loss of revenue, but in an industry where social influence and peer reviews have a high impact, there is a cascading effect through loss of referral business. This can, in turn, impact future customer acquisition costs and marketing efforts as well.

Knowing the metrics that impact your churn can help you better understand why your customers are disengaged. By identifying potential churners before they leave, retailers can take proactive steps to neutralize it.

Breadth of Purchase (cross-category): Breadth of purchase refers to the variety of products that a customer buys. Lower cross-category purchases can indicate customers who are not deeply engaged and may simply churn for a better price point.
Customer Complaints: Who is complaining and why? Measuring this metric can help you know how close a customer is to churning. Complaints that are common to a category or product can be early indicators of larger concerns. Additionally, mentions in poor service, in-flexible returns/ exchange or frequent complaints can indicate that a customer is looking for other options.
Feedback Scores/ NPS: Churn is closely tied to customer satisfaction, so Net Promoter Score (NPS) which measures the willingness of customers to recommend a company’s products to others can be used to understand high or low levels of customer brand loyalty.
Repeat Purchase Rate (RPR): This tells you the effectiveness of your marketing strategies and retention programs. Brands can isolate one-timers and focus their energy on making them visit again, which can yield higher gains than trying to acquire new customers. The revenue from a single repeat shopper is equal to that of around 6 new customers.
Repeat Purchase Probability: Different from the RPR, the RPP is closely related to the churn rate, as customers less likely to make another purchase are more likely to churn. In fact, studies have shown that conversion rates of repeat customers are much higher – a customer who has purchased twice in the past is about 8X more likely to convert than a first-time shopper.
Customer Lifetime Value: Customer lifetime value is the future profit your business can earn from its relationship with a customer. This is based on past purchasing behavior and their likelihood to remain engaged with your brand; it is a key metric to identify your top acquisition channels and optimize your customer interactions in a way that prioritizes your best customers.
Recency: An important and often under-rated metric, recency is the time since the last transaction. By segmenting customers on recency, you can detect the impact of marketing on purchases, and filter customers most likely to churn. This often isn’t as straight-forward as it seems, because in fashion customers don’t return at fixed intervals. However, if a customer that always visits during events such as Back-to-School or Black Friday sale doesn’t show up, it is a cause for concern.
Average order value: Average order value measures the average amount of money a customer spends per purchase or average basket value (size) per order. By understanding the basket size trend, retailers can spot anomalies in purchase behavior and identify if the customer is showing early signs of churn.
Product Reviews: Product reviews are often an indication of customer satisfaction. Consistent poor reviews could indicate that a customer is in search of other options, and also create a wider negative impact, making it harder to acquire new customers.
Profitability Per Order: In addition to business success, a high PPO can also indicate if only higher margin products and full price merchandize are being sold, giving you information on whether your tactics are successful or whether churn is eroding your profits away.
Purchase Frequency: How often the average shopper makes a purchase indicates how engaged they are. A customer returning frequently for their needs indicates you are top of mind and are better positioned to drive higher revenue from them.
Time Between Purchases (TBP): The gap between purchases within a one-year period is the time between purchases (TBP). Knowing this value can you give you insights into buying patterns and segmentation, enabling you to better understand which groups are churning.
Redemption Rate (RR): The percentage of loyalty rewards being redeemed is your redemption rate and a direct indication of your customer engagement levels. The average rewards program sees a redemption rate of 14%, and retailers that are too far off from their benchmarks need to when to act to tailor the program.
Product Returns: Like customer complaints, measuring product returns can indicate which categories are likely to make a customer churn. A high return rate from an individual can build up dissatisfaction with the brand and reduce their chances of shopping again. Additionally, social feedback on product quality shared with peer groups can have a high impact on churn.

The importance of controlling churn

Fashion retailers find it especially hard to identify customers at risk of churning – shoppers don’t buy at fixed intervals and loyalty is at an all-time low. Customers that seem to be disengaged might still be interested in your brand, just not reached their re-buy period yet.

Investing in customer retention programs can make all the difference to business growth. According to Bain & Company, increasing customer retention rates by a marginal 5% can increase profits by a whopping 95%.

It is therefore critical to study the behaviors, interactions and experiences that are driving the customer relationship, to measure churn and take effective steps to minimize it.

Manthan enables marketers to execute comprehensive churn management programs. Custom-built for the retail industry, our responsive algorithms, and AI-driven execution capabilities provide marketing teams with the insights and control they need, to proactively engage with at-risk customers.

For more information, read AI Driven Approach to Boosting Customer Retention in Retail

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 Steps to Mastering Customer Journey Focused Marketing in Retail

Consumer-facing businesses are crippled by every-growing touchpoints and siloed systems that don’t speak to each other. A complete overhaul of these legacy technologies isn’t an option – it costs prohibitive with a long-drawn-out time to value.

Yet, they need a way to quickly connect these individual systems for an end-to-end view of the customer journey analytics. Customers today hunt down what they want at the price they want. They are open to sharing their data and being followed in their purchase journeys if they get value in return – whether it is in the form of better experience, elevated service, convenience, special offers or exclusive treatment.

For marketers, delivering on heightened customer expectations requires the use of customer journey analytics software at scale – acting on customer moments as they happen. Instead of reacting to customer-created journeys and go where they are going, you need the tools to manage, influence and even mold their routes to maximize their experience and your sales. Offering guidance, timely reminders (and perhaps a special offer) to customers who are wavering during the shopping process can yield significant business gains – reduced cart abandonment and improved conversions.

With more digital channels getting added to the mix, customer decision journeys are now maze-like.

We suggest five essential steps to set-up your customer journeys, without having to start-over:

Collect all customer data – chances are you already have this in different systems. It is important to include all browsing and transactional data, irrespective of whether the journey resulted in a purchase
Connect the existing data sources – Given the expansiveness of digital journeys and the variety and volumes of data, this isn’t likely to be easy but forms the foundation. This includes cleansing, organizing and resolving identity disputes. Given that retail business, today is as much about the seamless flow of information as it is about the flow of goods; it is important to have good data governance in place. Consider a Customer Data Platform that unifies all customer data.
Switch on the data science – Start with connecting touchpoints during a single journey in real-time; to analyze customers’ deflection points, unusual behaviors, channel preferences and the kind of information they seek during their moments with your business. Some customers see more images, some are interested in product information, others read more reviews, while some other scout for offers. These elements are important to truly understand customer behavior and underlying reasons for jumping channels and device.
Follow-up with the right messages. Now that you know where customers are in their journey and what they value, personalize interfaces such as email, website, mobile app. Customer insights, together with your expertise can help serve meaningful messages on the relevant channel that moves customers towards purchase decision or even do away with a likely hurdle as a special case (such as high shipping fee, few payment options etc.).
Visualize journeys across the entire customer lifecycle. Connect a customer’s entire relationship to know whether she is just warming up to your brand, a repeat buyer, a loyal customer or at-risk of moving to your competition. This insight can be used to nurture the relationship over time and achieve upsell and cross-sell. Machine learning and predictive models iteratively improve journey performance and assess what individual journeys she is on at present, and what impact these have on each other and on the lifecycle.

In all of this, it is critical to continuously form and test hypotheses, measure outcomes and re-calibrate tactics for maximum impact. By automatically getting data-driven answers to questions such as ‘what kind of messages will further the customer’s journey’, ‘what is the best time to communicate with a customer to incite a favourable action’, and ‘which channel is likely to be effective for a wavering customer’, marketing analysts can maximize returns and optimize spend.

Conclusion

Today’s competitive retail environment requires a Journey Analytics tool that is built for scenarios specific to retail. It can enable marketers to create impactful customer-centric campaigns that target their path-to-purchase and measure journey targeting outcomes.

It is important that customer journey analytics tools automatically identify the best paths for each behavioral segment, with technology doing the heavy lifting of picking the most-suited channel combinations (basis both customer and fashion retail context) and best moments to communicate, with the end goal of accelerating conversion. The system should automatically be able to place customers in the precise lifecycle stage and move them towards the desired stage and outcome through micro-journeys along the way.

Getting these critical components spot-on and combining them with predictive insights can help you drive revenue. The results are bound to be superior to workbench-based tools, that only allow creating pre-defined paths and cannot react in real time to customer behaviors and actions.

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).

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.
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.
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.
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.
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!

Manthan named a Strong Performer in Forrester’s Customer Analytics Wave™ Q2 2018

Modern CMOs are striving hard to build relationships with their customers that are deeper than those with their competitors and are transforming their customer marketing practices. They are moving away from mass marketing to targeted, hyper-relevant content, across all customer touch-points in the moment of truth. To consistently succeed with hyper-relevant personalization, CMOs are looking to establish rich capabilities for Customer Analytics, Data Management and Omnichannel Execution.

Having served retail and consumer businesses for over 14 years, Manthan brings a unique mix of deep industry experience and data science expertise that has translated into specialized customer analytics offerings for every segment from grocery to fashion, specialty and restaurants and for every format from department stores to convenience stores.

Manthan has now been recognized as a strong performer in The Forrester Wave™: Customer Analytics, Q2 2018

To assess the state of the customer analytics solutions market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of the nine most significant customer analytics solution providers. These vendors were evaluated against a comprehensive set of 39 criteria, which were grouped into three high-level buckets: ‘current offering’, ‘strategy’, and ‘market presence’.

Forrester states that Customer Analytics solutions are “a new breed of analytics technology” that “perform common customer analytics techniques automatically, emancipating insights from customer data with ‘do-it-for-me’ (DIFM) capabilities”. According to Forrester, Consumer Insights professionals and their stakeholders “want easy and immediate access to insights”. Those adopting customer analytics solutions benefit from ‘Business user accessibility’, ‘speed to insights and action’ and ‘single view of the customer’. We couldn’t agree more.

A holistic understanding of the customer behavior and responding to their motivations and interests is critical for consumer facing businesses today. Manthan’s customers across retail are using Customer360 and TargetOne for use cases such as personalization, multi-channel marketing, location-based targeting, and product recommendations.

“Manthan specializes in problems unique to large retailers”, “It does a good job of addressing the three distinct personas in the analytics value chain — business users, data scientists, and data engineers.” -The Forrester Wave™: Customer Analytics Solutions, Q2 2018

According to Forrester, rapid deployment to match user needs, automatic insight identification, strong model monitoring and analytical transparency are key factors professionals should look for when considering a Customer Analytics solution.

“Manthan’s robust customer data model underlies prebuilt KPIs and predictive scores, which retailers and other direct-to-consumer brands find alluring.” -The Forrester Wave™ Customer Analytics Solutions, Q2 2018

This market is characterized by a high level of complexity with many different technologies and providers. Manthan differentiates itself by helping business users consume the insights out-of-box without having to deal with complex algorithms. Some of Manthan’s customers across the globe include Comcast, Payless, Les Schwab, Future Group, Big Y, Robinsons Supermarkets, Chedraui, Consum, Yum Brands, Charming Charlie and FairPrice. This recognition definitively puts Manthan on the Customer Analytics map, as it further elevates how it serves its customers; to understand, engage and wow their end-customers.

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?

Articles in Category: Customer Analytics