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The Editorial Board comprises technologists, data experts, thought leaders and marketing gurus. We are dedicated in helping business leaders unlock the true potential of analytics.

Sort it Out. The need for Customer Centric Merchandising Assortments.

September 26, 2016 12:54 pm No Comments September 24, 2024

Retailers are under tremendous pressure because of channel proliferation, fickle consumers, increasing price sensitivity and lack of brand loyalty. In order to attract new customers and retain existing ones, retailers must understand customer tastes, trends, buying patterns and tune merchandise assortments accordingly.

Here are a few reasons why building a 'Customer Centric Assortment' strategy is certainly key to Retailer’s success in today’s dynamic environment.

The need for a seamless omnichannel shopping experience

Consumers have become even more demanding than before. Thanks to the internet and social media, consumers are much more aware of what is available, prices across retailers, latest trends; and want the convenience of buying anytime and anywhere across the multiple channels at their disposal. A customer may start browsing products online but choose to buy in store or vice-versa. The number of people shopping online on mobile devices is rapidly growing. A retailer must be reachable across various channels – mobile apps, websites, stores, social media; and be able to recognize and provide a seamless and consistent experience to the customer across channels. In addition, customers have come to expect a personalized experience catered to their tastes, preferences and latest trends as the norm.

Brand Loyalty no more

Customers’ shopping behaviors exhibit “channel-hopping “ which involves looking up products in store, studying reviews and comparing prices before closing in on the “best deal” online. Brand loyalty is under constant attack in developed economies and virtually non-existent in developing economies, especially when buying certain products like fashion and electronics. Customers are both product and technology savvy and extremely fickle. “Social Media influenced buying” is on the rise – heavily influenced by peers groups. In addition, flexibility of payment options is key differentiator for big ticket items as well as aspiration buying.

To be able to succeed it is important to be able to manage operations across multiple channels, have a complete understanding of customer buying behaviours while having a single view of the customer. Thereby, customer centric assortments can directly translate to steeper sales and profitability, stronger brands and more loyal customers.

Learn how you seamless integrate analysis and insights into action and make the shift from Product Centricity to Customer Centricity on this interactive customer centric merchandising page.

Assortment Optimization

Achieving a Single Customer View in E-Commerce

July 12, 2016 9:54 am No Comments July 12, 2016

What is a Single Customer view?

Till some time back, a single view of customer meant bringing together all data for customer and consolidating into a single record. Driving force behind single view of customer within an organization was usually operational. Marketing was merchandise driven and strictly followed quarterly planned marketing calendars and hence was content with only half updated or even outdated view of a customer.

In the present scenario, businesses have become multi-channel. Customers now interact across a range of touch points. Marketing has become customer centric and is not content with an updated view of customer. They need to know each customer interaction and the intent of each interaction to personalize and ensure contextual relevance of each communication. For businesses today, the single view of customer means bringing together all data of customer including interactions, transactions and intent.

Data Challenge

Rise of digital commerce has resulted in not just huge data volume but also in new type of data that businesses have to deal with. Visitors can browse through the site, view products, read reviews without providing any personal information. Shoppers can interact with a brand’s social media properties, like posts, comment on them or share within their network. Visitors can browse through product catalog on the move through their mobile phones. These all are examples of data that is dynamic and relevant only for a limited time period and hence speed of capture and update is extremely important. Achieving single view of customer should not be looked as a one-time project, but as a continuous process of data persistence, update and correlation. Business use case behind the data types should help define the data capture process and technology.

Rise Of Contextual Marketing

Context is the key to relevance and personalized experience on e-commerce channels. Context can be geographical region, time of day, items or category you are viewing, brand or the price of item. Businesses that lack contextual personalization will soon risk customer attrition. The foundation for contextual marketing lies in real time integration of contextual data against every customer and having execution systems leverage this foundation to build personalized customer experience across digital channels. Contextual marketing brings along the challenge of real time update of single view of customer.

What are your options?

The path to single view is a long and arduous journey. Here are a few key points:

– Manage multiple identifiers: Cookie data, device data, social handles, email ids, loyalty numbers

– Maintain interactions per channel: Persist interactions per channel such as browsing details, purchase details, social likes and shares, etc

– Continuously correlate data: Continuously look for common identifiers across data and collapse them. For example – use social login on ecommerce to collapse social and cookie data. Collapse cookies data with customer email address during item purchase. Use device id and multiple logins to build household information.

– Identify actionable interactions: Identify interactions which are opportunities to provide contextualized marketing. Deploy real time integrations and actionable systems that can combine contextual information with customer past history to provide a highly relevant personalized communication.

Businesses are becoming customer centric. A single view of customer, will be the most important asset that a business could hold to be successful in the new digital commerce era.

Retail customer loyalty programs

How Big Data Improves Loyalty Programs

June 29, 2016 6:09 am No Comments June 29, 2016

Until a few years ago, most retail loyalty programs were all about handing a card with a Rewards number with primary focus on rewarding shoppers based on their spend. Reward typically implied reduced pricing achieved in the form of (i) redemption of earned points towards a purchase, or (ii) a deeper discount for being part of the loyalty club, or (iii) simply access to ‘special’ member pricing.

With increasing competition, introduction of new channels of shopping (brick and mortar vs. online), increasing number of millennials driving consumption; retailers are being pushed to rethink their loyalty program. While retailers may think loyalty is dead and shoppers are simply seeking for the best price, several studies indicate that the new age shopper tends to be loyal to brands/retailers – they are not loyal to just ONE SINGLE retailer for their shopping needs.
Today’s shoppers do not equate loyalty program to a discount alone. They seek a loyalty program which offers value. This value implies:

Personalized benefits:

Receiving product or offer recommendations that’s relevant to his/her purchase needs. Shoppers want discounts, but also want personalized service or attention which improves their shopping experience. Examples of personalized services could be (i) free valet parking, (ii) free delivery service, (iii) access to a personal shopper or stylist, (iv) express checkouts for high spenders.

Convenience:

Ability to redeem rewards earned in any format. Gone are the days when shoppers carry printed or digital reward coupons to be able to redeem the rewards. Shoppers demand that retailers know them and are able to pull up and apply the relevant rewards/offers earned towards the purchase.

Timely rewards:

Ability to redeem rewards timed to shoppers’ purchase cycle.
While shoppers have become more demanding, they’re also more open to sharing their information in exchange for value.Big Data platforms, solutions and services can help marketers redesign their loyalty program that can create a win-win offering for both the shopper and the retailer. Here’s how:

Data Capture:

Big data platforms give retailers the ability to gather and store large volumes and different kinds of data. Big data solutions can store not only structured data such as shoppers’ loyalty rewards earn/burn and purchase transaction history but also unstructured data such as online browsing history, feedback/survey, shopper call logs and engagement on social media brand assets. These different data elements help retailers get a comprehensive view of their shoppers.

Data Analytics:

Big data solutions can execute analytics at scale and quickly generate different shopper personas with richer insights. Based on shopper personas, marketers can define loyalty programs to achieve the desired behavior – i.e.: (i) increase repeats, (ii) increase spend; (iii) acquire; (iv) retain at-risk of churn/churned shoppers; (v) increase profits. Simultaneously, marketers can identify shopper personas to map relevant incentives to drive behavior – (i) offer discounts and/or (ii) offer service perks.

Process automation:

Big data technologies have made it possible (i) to automate the data collection and storage process, (ii) run analytics to deliver shopper insights quickly, (iii) deliver personalized product or reward to each shopper based on his/her channel of engagement , purchase history, intent to purchase and timing, and (iv) to track metrics and change in customer behavior to measure the success of the loyalty program.

How are you using Big Data to improve your loyalty program? What challenges do you face? Share your thoughts ……..

Related Solution: Transform your Customer Marketing with Manthan’s Customer Analytics Solution

Disney retail customer experience

Top Ten Ways Disney Sets The Bar For The Retail Customer Experience

June 17, 2016 8:51 am No Comments June 17, 2016

As I am preparing to take my two young boys to Disney I am struck by what we in the retail space can learn from the Disney retail experience. Wait! Disney is not a retailer. It doesn’t matter. The customer experience Disney provides IS a retail experience. Here are some moments of excellence and how we can adopt them into the retail space:

1. Recommendation Engine (with real people!):

Booking online, a chat agent was immediately available to assist me with my room and category needs. Of course in retail this translates to ecommerce chat to assist with size and fit.

2. Up Sell:

Immediate email confirmation of my booking with links to everything I might want to do (purchase a PhotoPass as an up-sell, book Disney Dining to increase basket size and book advanced ride passes to guarantee a magical experience).

3.Single view of customer :

Every single booking I’ve made ranging from restaurant to ride passes to PhotoPass are all in my single customer record. Disney knows who I am and what I plan to do and then makes suggestions for what I might want to do next.

4. Logistics:

My Disney bands and luggage tags arrived within 48 hours of my reservation. It exceeded my expectations. Oh and they were sent in a plain box – no spoiler for my kids who didn’t know yet. Retailers can learn from BOTH of these. (Thanks Amazon for ruining Christmas present surprise by shipping an item in its box with a PHOTO of it on the outside of the box.)

5. Ship to Store:

Wait you say, Disney isn’t shipping anything to a store! Well their online check-in option is similar, I can check in on line make my room preference known and when I land in Orlando my room number is texted to me and my Magic Band will be switched on to open my door.

6. Loyalty Card/Payment:

The Magic Bands provide a loyalty card/payment option for me. No need to recite my phone number bring a credit card, or provide another ID. It is a simple way to get into the park, get into my room, pay for dinner, and buy merchandise in the shops at the property.

7. Useful App:

The Disney app tells me when what restaurants have availability and ride wait times. It is appropriate for the media (phone) and relevant to my geo-location and needs. It also tells me where Mickey and Donald will be at what time and how long the wait is for their autograph.

8. Householding:

Disney knows my kids and my husband and knows we are all a single household. It allows me to book different kids on different rides and have a Single view of our plan. In retail imagine if I could get recommendations for clothes or food based for each member of my household appropriately and under my control.

9. Appropriate Meaningful Well Organized Content:

Disney has the ability to search restaurants and rides by preference and posts restaurant menus and costs and height limits for each ride. The website is easy to navigate on my PC, Mac, iPad and iPhone.

10. Magic :

The entire Disney pre-trip experience has been flawless across all channels (phone, email, web, mobile and app). It has lived up to its brand promise of Magical.

Stay tuned for the trip report ….

Related Solution: Transform your Customer Marketing with Manthan’s Customer Analytics Solution

Top 5 Analytics trends

Top 5 Analytics trends in Fashion Retail

June 14, 2016 10:19 am No Comments June 14, 2016

Technology has enriched the overall customer experience. As a result, today’s leading fashion houses are looking at several ways to utilize emerging analytical technologies in fashion retail today.

Let’s look at some of the ways this is happening today.

Digital Marketing and Social Media Analytics

Digital marketing analytics expenses increased by 60% in 2015 as branding and advertising businesses boomed. Social media and online advertising on mobile will continue to grow as integration of offline and online customer experience is on the rise. This in turn, has increased consumer brands’ ability to digitally influence customers and digitally empowered customers’ ability to influence brand image and value.

Cross-selling and upselling through Personalization

Owing to advancements in technology combined with the avalanche of data available today, enterprises across industries are leveraging inexpensive technologies such as Hadoop to analyze huge amounts of customer data, understand patterns and subsequently personalize their offers to their customers. This in turn helps them out-think and out-do the competition. “More data storytelling equals more engagement”.

A leading Indian retailer boosted category growth by 50% with tailored campaigns based on affinity analysis, cross promotion between categories like kids, baby world and toys.

Strategic customer segmentation enabled the business to drive a consistent marketing strategy across all concepts. Customer acquisition, retention campaigns and maximization strategies enabled increase in customer engagement and loyalty. By understanding customer attitudes, their purchase behavior and identifying fashion trends, they make smarter marketing decisions.

Sales in select categories grew by 92% with targeted campaigns, using models like Market Basket Analysis, K-Means, Churn and Propensity.

Read more in Gartner report on Self Service and Advanced Analytics.

Predicting future trends of fashion styles

Analytics is helping retailers aggregate fashion trends and sales information from a wide variety of sources around the globe—from retail sites, social media, designer runway reports, and blogs covering trends—and making it accessible in real time –  across menswear, women’s wear, children’s apparel, accessories, and beauty.

In addition to the ability to combine both internal and external data sources, users now have access to more context for their data, which ultimately leads to more insights and better decisions.

Managing seasonal fluctuations

Fashion retailers often struggle to quickly address seasonal fluctuations and capitalize on unexpected opportunities. A lot of competitive advantage is to be gained in innovations that help retailers get the best bang for their buck, while maintaining customer loyalty.

With the right real-time insights, retailers can shorten seasonal cycles to meet changing customer preferences. This in turn, can help negate surprises in customer demand and minimize losses.

By using analytics, fashion retailers can have more flexibility in their supply chain responsiveness. Greater precision with in-season control can also be enabled by using modern analytics solutions to derive insights and optimize the 5Ps – product, promotion, pricing, placement, and people.

IoT devices in the fitness segment

While health and wellness have always been popular, it has become a trendy lifestyle choice for many over the past few years, driven by wearable technology.

Smart technology is embedded into clothing, sportswear shoes, and trackers. The large amount of information obtained from these connected sensors when combined with contextual data can result in highly useful and interesting insights for consumers.

Tracking the number of steps taken each day and heart rate, monitoring blood glucose levels, telemetry, and weight are just a few examples of data that consumers can obtain from their wearable devices. Consumers have started responding to such performance feedback and analytics from wearable fitness devices, if that’ll help them lead an active and healthy lifestyle. By using beacons and real-time analytics, retailers can create innovative engagement campaigns to further enhance the lifestyle choices that their consumers make, boosting loyalty as a result.

Manthan’s ready to use, cloud based analytics solutions are designed to address the unique business processes and nuances of the fashion industry. Leading retailers across the globe use Manthan’s advanced and predictive analytics capabilities to improve decision making, accuracy, speed and impact on all aspects of the business, from customer experience to merchandising optimization, operational effectiveness and multi-channel business orchestration.

Read more on how your business can stay ahead of the top 5 trends in Fashion retailing.

*https://www.audiense.com/steal-their-style-7-fashion-brands-bringing-life-to-their-digital-marketing/

Fashion Retail Analytics

The Key to Innovation in Fashion Retail – Analytics.

June 14, 2016 10:06 am No Comments June 14, 2016

Across retail, technology is helping push innovation and business competition to greater extents than seen earlier. The use of analytical platforms that are geared towards multi-structured data sources is helping drive faster and almost real-time customer responsiveness. App-like interfaces are designed to be simple, contextual and intuitive for problem-solving by an average non-technical user.

As a result, we are seeing a greater adoption of mobility in the use of analytical technology in-store, at the location, or even customer-facing apps. Fashion retailers, in particular, are diving deep into technology that enables supply chain responsiveness, visibility, and collaboration.

Let’s look at some of the ways improved analytics is driving business innovation in the fashion industry.

Customer Centric Merchandising:

Greater ability to integrate customer insights into product preferences, geo-demographic variations, a better understanding of lifestyle and psychographic drivers of sale – all now manageable at granularity and speed.

Demand-side:

Better management of product lifecycle using better forecasting techniques, more diverse data sources like digital and social data in demand analysis, advanced algorithms for predicting likely change in demand.

Supply execution:

Ability to shorten the business and seasonal cycles to meet changing customer preferences, enabling more flexible supply chain responsiveness, greater precision in in-season control by optimizing 5Ps and minimizing losses.

Store Operations and Customer experience:

Better in-store customer experience with analytics-guided selling and relevant customer engagement, greater real-time integration of multi-channel demand fulfillment needs, the personalized recommendation at the point of sale for loyal customers.

Pricing:

Better in-season control for full price sales, better markdown optimization, price consistency across channels, price control in promotional periods.

Promotional analysis:

Better execution of in-season activation programs/campaigns, the ability to deliver personalized communications on digital devices based on preference, advanced segmentation, history, geo-location etc.

Customer advocacy:

Ability to generate insights from customer advocacy programs, fashion ambassadors, activation programs etc, to enable a deeper understanding of demand, emerging trends & topics, responsiveness in digital engagement.

Digital shopping tools:

Virtual shopping assistants providing product recommendations based on preferences, customer inputs, and history, tools that help to create personal profiles, fitting sizes, and preferences in relation to occasions.

Subscription models:

Ability to try and buy new products, targeting the right offers to specific customer profiles, fashion expert recommendations in a box.

With Manthan Retail Analytics, fashion businesses can:

  • Optimize buying, selling, pricing, assortment, placement.
  • Manage product lifecycle pricing, sell-through and optimize markdowns.
  • Identify top-sellers to buy and promote better.
  • Anticipate and address out-of-stock before sales loss.
  • Compare sales, margins, return rates by vendor/products.

Read more

Big Data Marketing

Big Data and Marketing – Heady Cocktails And Crushing Hangovers.

June 14, 2016 4:02 am No Comments June 14, 2016

A marketer and a data scientist walk into a bar…

In most real worlds, they don’t acknowledge each other, perch on different “clicca qui” stools, chug different drinks, and go their separate ways.

The more data explodes, the more decision making practices remain the same. I was recently talking to a COO who described his role as the ability to take the most impactful decisions with the thinnest possible information. It is the nature of data – Big or Otherwise. We keep talking of social feeds and Facebook posts and mobile phone penetration. All of these make for great story telling. But unless the information extracted from these sources is explicitly useful taking a marketing decision – in talking to a customer, creating a campaign, or driving a cross-sell, it is of limited value. It is this absence of a meaningful connect between data sciences and marketing that we need to bridge.

What Big Data technologies help with, is to fulfil and make this crying need for usable, timely and relevant insights come alive.

Let me give you a few examples :

For a premier online fashion brand, the challenge in engaging with its customers centred around getting the relevant style lines in front of customers who are most likely to appreciate them, and therefore buy them. There is an intuitive understanding that the sporty maven aspires to a different style statement than the classic executive. The way the former navigates the site is of course different from the latter. The challenge and opportunity lies in identifying and quantifying this intuition, translating it into measurable insight, identifying the customer trigger, and therefore personalizing the web page. This is true Big Data analytics in action – analysing very large volumes of historic data, recognizing customer browsing behavior in real-time and serving the most relevant style-lines instantaneously.

Another great example is that of a big box grocer rejuvenating his weekly promotional newsletters to be more relevant and engaging to his mailing list, and therefore growing the traffic and conversion in his stores manifold. Creating variants of newsletters or creating split runs, have always been around. But getting every single customer to have a unique, different and most relevant mix of offers and announcements on the first page of her FSI is suddenly very special. It is a special marketing capability and it makes the customer feel special. But to do this, one has to marry behavioral insights with product propensities, the offer bank, and the campaign objectives of that week. This is Big Data coming alive. And it suddenly drives redemption up three times and campaign lifts into high double digits. Now we have a real example of how Big Data has transformed a simple weekly promo mailer into a competitive weapon.

In both these examples, what is valuable to note is that marketers have always wanted to do this – to engage the right customer with the right message through the right medium. What held them back was the daunting nature of the task. If done manually, the sheer permutation of communication options would call for an army of analysts to be deployed behind the scenes. If done using conventional technologies – the skills, effort and investment needed to get the data together, run the analyses, and integrate the different systems together to make it work, make the effort seem unmerited.

In both examples, all three critical pieces of the puzzle – getting the data together, running sophisticated analysis behind the scenes, and automating the entire process so that results can be delivered instantaneously – is achieved by Big data technologies and Big data analytics.

There’s a lot written on words like machine learning and artificial intelligence to prop up capabilities such as the ones I’ve described above. I for one am happy if the marketer and the data scientist share a tipple and reminisce the good new times.

Related Solution: Transform your Customer Marketing with Manthan’s Customer Analytics Solution

3 Levers to boosting productivity of the next-gen data scientist

3 Levers to Boosting Productivity of The Next-Gen Data Scientist

June 7, 2016 10:37 am No Comments June 7, 2016

Data Scientists in every sector are grappling with the implications of Big Data. Data Scientists are facing it difficult to deal with the increasing volume, type and detail of information captured by enterprises. The use of video, emojis, text in social media, and the range of information Internet of Things emit every minute will fuel exponential growth in data for the foreseeable future.

In my day to day job, I interact closely with the data scientists. These are some of the brightest people, looking to solve the next set of business challenges. Unfortunately, many of them are a frustrated lot today because of three key challenges they face:

 

Poorly defined business use-case

The data scientist is expected to do some magic, identify hidden patterns in data and provide ground-breaking insights. Unfortunately there is no magic in data sciences – that is why it is called a “science”. The basis of a successful project is a well defined business use-case. The selection of data or the statistical techniques are secondary. Anything the customers, business users can do to concretely define the scope of work and expected outcome, the more effective the data scientist can be. So always start with a business use-case and not a statistical technique. Avoid being a hammer looking for a nail.

Data

This is probably the most non-value-add activity a Data Scientists, and something they are not trained in the first place. The foundation of clean connected data is critical for a successful outcome. Data cleansing, connecting keys across data sources, and managing the data set is a significant amount of work that requires special skills. For example, In the customer analytics world the ability to draw signals from customer data from one channel and predict their behavior in another channel is the holy grail. People want to know what is the value of a positive review on their brand page. If the social marketer could quantify that a customer who leaves a positive review is twice as likely to become a high value customer, he will be able to justify the value of social marketing. The data scientist can do this, but they need a single-view-of-the-customer to start their work. Today the data scientist is spending 70 to 80% of their efforts getting the data and not doing their real job. Avoid “garbage in-garbage out” analytics.

Model management

Once the data has been reasonably organized the data scientist can develop model that are suitable for the use case at hand. Now the challenge of moving the model into production becomes a bottleneck. Typically advanced data scientists work with their own preferred tools. Some of these tools are flexible and allow quick development, but are not meant for a large scale deployment. They lack the ability to run at scale and in production environment. With multiple models in play, the data scientist also finds it hard to manage all of them. This is where industry standards like PMML and advanced customer analytics platforms come into action. Once the model is developed it can be converted into PMML format and can run in any modern customer analytics platform. Using such formats and platforms enable the data scientist to quickly move models into production without any bugs creeping in at the last stage. It also helps them in sharing results with the business users and automating the usage of their models in a salable manner.

Today, businesses who believe analytics can be a key competitive advantage should focus not just focus on buying the shiniest piece of technology in the market place but should spend effort on addressing these 3 challenges. Fixing them will have a dramatic improvement in the accuracy, development times and adoption of analytics across the organization.

Customer Lifetime Value

Increasing Customer Lifetime Value (CLV) in e-Commerce

May 3, 2016 11:14 am No Comments May 3, 2016

Though the amount of sales made is crucial in the ecommerce industry, it’s the ability of the e-tailing company to make more sales per person that edges them past their competition. Businesses which look to increase the customer lifetime value of their customers through proper understanding and analysis of their behavior and purchasing patterns are sure to get a larger chunk of the ecommerce market.

The key here for businesses is to engage proper retail analytics which can directly help them to target their customers more effectively and increase their customer lifetime value to the best possible extent. Let’s see what businesses can do to increase the lifetime value of their customers:

Customer segmentation

Most e-commerce sites have special offers and discounts for regular shoppers and another kind of offers to attract first-time buyers. The likeliness of selling to an existing customer is around 60 to 70% and to a new customer, it is around 5 to 20% as per Marketing Metrics. The objective is to reach out to new customers, but more importantly, keep the regular customers more loyal. Customer segmentation shall not only be based on the regularity of purchases made, but also by having a holistic view of customer’s behavior, type and value of purchases made, the frequency of purchase, and so on. How efficiently businesses can extract this information through smart retail analytics will make customer segmentation more accurate and relatively more effective with retail sales.

Identifying right sales timing

In most cases, it’s the timing of the sale rather than marketing which is crucial. With retail sales now beyond the traditional sales frenzy months in a calendar and virtually throughout the year, businesses are forced to look at every opportune moment to make the offer. With a more vivid view of the customers’ behavior, businesses can not only make the right sales offer but also make the right cross-sale or up-sale proposition which can most probably prove beneficial for both the shopper and the retailer.

Personalized promotions

Fading are the days of generalized promotions. The online world is moving towards more of the segment based or more personalized sales promotions which work wonders. Personalized promotions give the shopper a feeling of being counted as an important customer to the company. This may at times aid in getting the sales done. Personalized promotions also give customers the feeling that promotions are actually aimed at his requirements rather than unsolicited marketing attempts. However, for retailers to be more effective with this they must have processed information about the customers through proper information gathering and analytics. Personalized promotions done through dynamic remarketing campaigns can effectively increase sales.

Identifying best channels for customer engagement

Not every marketing effort is successful and not every channel of communication is equally effective, moreover, one channel may be effective for one segment and ineffective for another. Businesses, through improved analytical tools, must gather information like knowing effective channels of communication-based on customer segments and customer demographics. Only this can give an insight into which channels to use for which segment and what kind of messages best work on these channels.

Optimizing interactive experience

The way you get shoppers to your website and the way they flow through your site to make the purchase — all put together makes the whole experience of the shopper. Now with many competitors making rapid and frequent changes to their interactive websites, you have to be proactive and constantly look for optimizations of your website. With proper analytics, you can get the browsing flow of your customer right from the landing page to the checkout page. You can then look for areas where they drop off, areas which push them to the next level, what they see at every point and how they would react. With these insights optimizing your pages for maximum yield can be extremely useful and effective. A/B testing and multivariate testing of your web pages can aid with right analytics.

Useful and informative feedback program

Never let your feedback system take back casual or normal information which doesn’t give you any actionable insights about customer experiences. A satisfied customer in most cases is ready to spend that extra minute to give you more useful and valuable information which can instantly help you. And an irate customer will vent out his anger to help you identify areas of improvement. You may find a way to cross-sell or up-sell or maybe get an immediate reference sale opportunity, or you can simply walk away with crucial information which can add value to your customer segment. Activity like this will also give out crucial information like the survey result mentioned below:

Proactive customer service

As per KISSmetrics, 71% of consumers ended their relationship with a company due to poor customer service. Nothing beats a proactive customer service, so avoid the generally expected service calls or communications that you associate with regular sales activity. Look for spaces where you can add more value, take that extra effort or make that call and be really concerned for your customer’s concerns. All this will not only make them more loyal but also let them open up to you in many ways. For this, you have to know when and where your customers require your support and what they are most expecting from you, and how you can surprise them with timely and proactive efforts. This again calls for predictive analytics which proves crucial in such situations.

Conclusion

The chance of a customer slipping away is highly probable with so many e-tailing businesses vying for market share. And it is reported that globally the average value of a lost customer to the company is estimated to be around USD 243 (KISSmetrics). And this makes increasing the lifetime value of a customer even more important in the quest for e-Commerce success.

Retail Data Monetization

Retail Data Monetization: Are you sitting on top of a retail goldmine?

August 18, 2015 5:30 am No Comments August 18, 2015

The majority of sales generated in the 4.5 trillion dollar US retail market is in-store and the volume of transaction data collected at various points in the trading process is immense. This data is a treasure trove of customer insight as well as product performance. While many retailers mine this data to gain specific insights into understanding their shoppers better, the possibilities that such data analysis opens up is largely untapped.

In an ultra competitive market, retailers can generate an additional revenue stream with proper data monetization techniques.

By sharing the data that otherwise sits idle in their internal systems, retailers can pave the way to a collaborative approach to sustain shopper demand while generating sizeable revenue regularly.

Real-time Granular Data Sharing for Actionable Insights

Jack Hoe, manages the data at a high end supermarket chain. Every quarter he religiously downloads terabytes of retail trade data in reams of excel sheets and shares those with a syndicated data firm. He believes this not only makes him earn a wee bit from an otherwise data dump but also helps build a better informed retail scenario.

Samantha, his counterpart from another major supermarket store has a different approach. She too shares the high volume data, but not with the syndicate. She shares it directly with the suppliers almost real time. Suppliers are willing to pay a fee for packaged insights since it helps them act quickly and boost category share. She has not only built a steady revenue stream from data sharing, the promotions her stores run and stocks that they manage to result in a better shopper experience.

Jack and Samantha represent the traditional and advanced data sharing models respectively. The data shared by Jack with research organizations is part of a high-level data dump across the sector which is analyzed and reported every quarter. These reports provide generic insights and come too late in the day for manufacturers and suppliers to tweak their strategies in real-time.

On the other hand, with the help of advanced and secure data sharing tools Samantha shares, not just data, but contextual retail insights in real-time, giving the suppliers an edge in demand forecasting, and promotion planning. No guesses here on who is ahead in the game!

Highly Targeted Promotions and Improved Promotional ROI

With retailers and their suppliers on the same page getting SKU level visibility, the chances of any product going out of stock is virtually nil. The contextual and relevant insights help suppliers in improving their category share by understanding product, category and shopper demand better.

As a result, shoppers experience much more targeted promotions, sales go up, and retailers and suppliers see higher promotional ROI. By directly influencing demand at the point of action, retailers and suppliers can thus drive sustainable growth.

Ease of Adoption and Quick ROI

The ability to mine data and gain contextual insights is the biggest differentiator in the race to stay competitive in retail. As per CGT-RIS Data Share study 2014, over 54% suppliers receive real-time data from retailers and more than 70% of both retailers and suppliers agree that data sharing has improved their sales and promotions while promoting a better dialogue. However, sharing raw data dumps doesn’t cut it for most suppliers. They will still need to make sense of the data, using analytical tools.

Suppliers could be more interested in accessing granular, packaged retail insights that are easy to consume. If provided through a centralized platform, and it helps them to increase revenue, why wouldn’t they be willing to pay a small fee to access these insights regularly?

Today SaaS-based collaboration and data sharing technologies are easy to acquire and quickly integrated into existing ERPs. Available through subscription models, the technology investment in building retail data sharing capabilities, that lead to easy data monetization, is well within the reach of retailers.

Retail data monetization has proved to be a cash cow for many retailers with a vast majority recovering the cost of the data sharing platform in less than six months!

If you were certain to earn 10 dollars just by investing a dollar, wouldn’t you do it? It’s not a magic pill, just the result of intelligent supplier collaboration.

Learn more about how Manthan’s Vendor Link can help you gain quick ROI from your supplier collaboration efforts.

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