As a marketer, I understand that customer data management is hard. Add to that varied new and legacy technology stacks, and the challenges increase. Despite understanding what plagues marketing functions, when I am the customer, I get agitated when a business fails to understand my basic preferences.

‘A promotion I receive on text messaging is not accepted on the mobile app, and the customer service team is clueless about it – this is unacceptable!’ This has happened to many of us.

The number of channels continues to grow, and businesses are trying to embrace cross-channel marketing. However, they fail to connect the various sources and channels.

It’s obvious that all marketing needs to be connected, and communicate a consistent message to customers.

The three components to consistent messaging

• Creating a unified view of customers across channels
• Surfacing insights and identifying marketing opportunities
• Orchestrating communications across offline and online channels

Connecting channels with customer data

The most important marketing channels for retail include mobile app, website, email, social, SMS, POS and direct mail. These are critical simply because you must be where your customers are. Marketers also agree that multi-channel retailers outperform single channel retailers, capture a higher share of customer wallet, and their customers have a higher lifetime value.

The impact of a Customer Data Platform

A Customer Data Platform forms the foundation of such a cross channel customer marketing platform. Without this in place, marketing orchestration is incomplete and superficial, as you fail to leverage every atom of data you have about the customer, their behaviour, transactions and past responses; and hence know only a sliver of what there is to know about that individual.

At one of the largest multi-format retail conglomerates, this CDP is serving as a strategic asset that teams other than marketing make use of – to introduce new product categories, plan their merchandising and to simplify customer purchase and post-purchase journeys.

Applying Intelligence to Data

Using predictive analytics and Machine Learning algorithms to translate this customer data into insights is where the intelligence lies – what is the customer likely to do next, where is the customer in the lifecycle (new, highly engaged, likely to churn, inactive), how do they respond to promotions, what is their lifetime value and so on.

The benefits of cross channel marketing also lie in building a clear picture of how marketing is driving results at an organizational level, rather than measuring performance at a channel or campaign level.

And this is how I want marketing to influence business and my company’s strategy.

 

Forrester defines cross channel campaign management as ‘Enterprise marketing technology that supports customer data management, analytics, segmentation, and workflow tools for designing, executing, and measuring campaigns for digital and offline channels.’

Retail marketing today is shifting from understanding what the customer wants and providing them with it, towards being a trusted advisor who adds value to their life. Imagine how pleasantly surprised a shopper would be if she came across that perfect stole she wasn’t even actively looking for.

But to enable such experiences for customers, retailers need an army of staff who are studying customer behaviour, preferences and activity all the time. Alternatively, this can be done seamlessly with data science. Artificial Intelligence and advanced analytics are pushing the boundaries of what is possible, equipping retailers to become truly customer-centric.

The More Things Change…

The last two decades have clearly demonstrated that the fundamentals of marketing remain the same-businesses that are able to consistently delight customers are the ones that succeed. What’s changed are the mechanisms used to achieve this. Pre-digitization, communications were one-way through mass media and only touchpoint was at the physical store.

Since shoppers now consume information differently and through multiple channels, marketers are changing as well. When it comes to campaign management, these 3 things that are a must for marketers:

1. Seamlessly Connecting Channels for a Consistent Experience: Managing customer interactions across channels comes across as a basic requirement, but to do this, marketers need to make sure channels and devices converge to offer a meaningful experience. This requires not just a customer data platform, but also a campaign management solution that is truly omnichannel.
2. Predicting Customer Behaviour and Activating Personalization: With segmentation, marketers can understand customers and their preferences. What differentiates a leader is their ability to predict behaviors, and activate those insights such that customers experience one-to-one personalization, like they did with their friendly neighbourhood store owner. AI based personalization is not limited to just basic variables such as gender and age, but uses deeper insights such as the individual’s lifestyle, the image they want to portray, how much they value exclusivity versus discounts, their life-cycle stage, and more.
3. Communicating with Customers in Real-Time: For retailers, being able to act on customer micro-moments is critical to positively impact conversion. This is where in-depth understanding of customer journeys come into play. If a shopper was browsing for a product, but did not purchase, it is easier to nudge them towards purchase with a timely notification or offering. Mobile in-app and push notifications are becoming increasingly important for engagement – it is real-time, easy to consume, and does not encounter the friction of channels such as e-mail.

The Right Campaign Management Solution Does the Heavy Lifting

The value proposition of data-driven marketing has changed. Earlier, data was used for reporting,  creating dashboards and keeping tabs of spend and revenues. Today, data is used to generate prescriptions and recommendations, so that you get the best returns from marketing while maximizing customer lifetime value.

This is the new era where only what needs attention gets highlighted, and best actions are suggested. The heavy lifting of data aggregation, insight generation and simulation take place behind the scenes.

A truly advanced campaign management solution can listen to customer behaviour and execute personalization in real-time.

Mobile app based customer targeting: The right solution should enable marketers to easily target customers who have downloaded a mobile app but not registered. Or even customers who have registered, but not purchased or haven’t interacted with the app in a few weeks. This can be highly impactful with the addition of rich media notifications and in-app personalization of banners and coupon wallets.

Location proximity-based marketing: Knowing when your customers are near can offer a tremendous advantage. Being able to send promotions to customers, based on their past purchase history, when they are nearby or inside your store, can draw them to make more purchases. For example, a customer in the golf shoes aisle might be interested in a bundling promotion you are running for golf tees.

Intelligent journey builder: Drip campaigns and sequential promotions can extend journeys using customer’s response on a channel or can be based on predictive micro-segmentation. Communications that are customised (aspects of when to send, what offer to send, and what channel to send on) to live user activity have a higher conversion rate. For example, events such as mobile app launch, cart updates, or even inactivity can be used to tailor the next communication, and move customers towards a purchase.

Test and Experiment within journeys: It’s hard to know upfront what channel, what time and what combination of copy, offer and creative treatment will have the maximum impact. With A/B testing, marketers can easily assess the performance of each component and arrive at the best arrangements. Similarly, test and control is a great way to measure the effectiveness of a new tactic.

A New Era of Delivering Experiences at Scale

Technology is evolving to make interactions contextually relevant to customers. By working with online and offline data (including POS, mobile, text messaging, e-commerce and email), artificial intelligence can manage it all, at scale, in a quick timeframe for tens of millions of customers.

And retail marketers who do this well will be able to form deeper relationships, enhance customer engagement and loyalty and retain their best customers to impact growth.

“What next?” is a question most marketers ask themselves every day. Some base their answers on experience while others rely on the gut. However today we see a new breed of marketing professionals that are basing their decisions on evidence, thanks to new technologies and prescriptive analytics companies that help make sense of the seemingly random data.

In the increasingly competitive fashion retail industry, where little can be left to chance, while planning is critical, the course of action for every shopper must be decided in a split second to capitalize on the micro-moments.

While predictive analytics tools gives you a view of what’s coming, prescriptive analytics models tells you exactly what should be done to make the most of the situation.

The Goliath in the Room

Machine learning has taken decision-making in marketing departments to the next level. Predictions and recommendations are being made by matching customer profile with product attributes. Easy to do with a handful of products, but virtually impossible to manage when customer segments and product categories go through the roof.

In fashion, for example, a purchase goes far beyond the basic attributes of color, size, fabric, design, and price. Sticking to basic attributes such as ‘t-shirt, blue, large, polo’ isn’t going to make the sale; there are hundreds of product subtleties that need to be factored in. Placement of logo, sleeve length, the texture of buttons – these features can make or break the sale for today’s discerning customers. While these are obvious to a consumer the moment they see the item, it’s extremely tricky for the marketing analyst to get right.

Driving Customer Engagement with AI

Given the ever-multiplying number of attributes, decoding data into actionable insights is now best left to AI driven analytics. Large retailers can predict what customers are likely to buy, and then prescribe the most efficient route to close the sale.

Last year, UK based retailer ASOS announced a significant improvement in predicting Customer Lifetime Value (CLTV) in marketing through the use of AI. The retailer built a model that classifies a given customer as valuable, and potentially how valuable based on signals such as customer’s demographics, purchase history, returns history, and web and app session logs.

(Fore)Seeing Patterns in the Data

The biggest reason for using AI is its inherent ability to perceive a deeper understanding of context, customer preferences and how they make purchase decisions. AI-driven analytics enables retailers to dive deeper into consumer data, automating recommendations for customers, providing them with information that is relevant and meaningful. Gartner’s Hype Cycle for Retail Technologies, 2018 mentions cognitive expert advisors (CEAs) as a technology with high benefit, with the potential to improve customer engagement by making recommendations and aiding decisions.

Crucially, this ace up the sleeve of retailers can bring about customer loyalty by helping them find what they want quickly through a comprehensive understanding of their preferences.

Impacting Bottomline with Predictive Analytics

By accurately predicting the product-likely-to-be-bought-next, and the kind of promotion that will appeal to the given customer, retailers can immensely impact customer loyalty and drive sales.

Effective ‘next best offer’ systems rely heavily on AI and advanced analytics and can drive personalization at scale.

To learn more about how you can improve customer engagement using prescriptive analytics models, Download our guide to driving next-best-actions in Retail.

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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”

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:

1. Increased revenue from existing customers through personalized customer engagement and special offers based on individual preferences.
2. 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.
3. 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

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.

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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

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:

1. 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
2. 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.
3. 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.
4. 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.).
5. 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.

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.