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Knowing your customer: from big data to quality data

Why knowing your customer has become so critical nowadays?


In a digitally powered, hypercompetitive retail context, it seems engaging with your target customers in a way that goes beyond the product or service itself is the formula for competitiveness. In an era of almost infinite choice, the product-centric (push) days, even for the luxury segment, are over, and retailers are heavily investing in reviewing their value proposition (aka building “brands with purpose”) and defining personalized experiences based on their vision of life.


But, before delivering any kind of personalization, the first step is having a clear understanding of the customer and her different journeys (yes, there usually are many journeys per customer segment, depending, for instance, on her consumption needs and occasions).


Current available technology allows bringing customer profiling to another level, from data capture to analytics to manage Gigas of data from many, most of them digital, sources.


The question is… how to find the right balance between the amount of data and return on effort to manage it? How to identify the “quality data” at the right stage of the personalization journey?


The more data the better. Go Big Data or die… or not?


There is no doubt that if you want to know your customer, you’ll need data.


In the Pleistocene, when retail chains were not that extended and, of course, the world was analogic, data was already collected by human CRM’s, also known as store owners. The tools they used to collect information were conversation and observation. Smart store owners, based on the direct interactions with customers, were able to create different shopper profiles and demand patterns on their minds so they were able to gain loyal customers and increase ticket value.


Then it came the retail chain phenomenon. Personalization was about offering a broad assortment at competitive prices and, as it was mainly a self-service model, any customer would “personalize” her shopping basket. The key at this stage was providing access to a wider choice.


After the grocery success, retail chains, of any segment, extended quickly and, as competition grew, retailers had to rethink the approach to personalization. Brick and mortar stores had a finite capacity and the one-size-fits-all model was not valid anymore. The pure product push model started to evolve. Retailers needed to define somehow personalized assortments based on the type of customers they had at each location. So, they recalled how personalization was delivered in the old times: conversation and observation. The issue was how to move from human, single-store to store chain-level machine-based CRM’s. Solving this challenge required, from one side, technology to capture, store and manage data and, from the other, a clear understanding of which data was critical to be collected as it meant true information about customers.


During several years, as technology for gathering data was not intensively developed, retailers relied on standard info coming from membership cards, massive campaigns, focus groups and panels from market research companies that pioneered the customer analytics journey. But personalization was mostly still about a more or less curated selection of products rather than experience.


But, in the last five years, the impact of omnichannel and digitization has been so huge in the retail industry that has completely redefined the concept of personalization (micro targeted experiences during the entire shopping journey). Due to that, the ability to know each of your customers has permanently been at the center of the stage since then.


Technology has evolved at such a pace that, apart from improving the legacy data capture and analysis models, now we can have access, at reasonable cost, to many forms to track and interact with customers in real time, such as face/sentiment recognition or geofencing.


So, nowadays we have thousands of ways to capture data. More accurate data. Real time data. Big data. The feeling that the more data the better to enable delivering personalized experiences has been spread across the industry. Therefore, retailers have started investing in setting up platforms and hiring data scientists to capture, clean and model as much data from their customers as possible.


But, what’s the impact of all this data in the customer satisfaction? Is all the collected data key to solve an actual challenge or create a new business opportunity that will generate revenues or profit? How is the return of the big data effort measured? Is it about having about as much data as possible or about translating key data to insights and actions? How many retailers are exploiting 100% of their available data?


Having a clear data strategy is the first step towards “quality data”


If retailers want to make sure they are focusing investments and effort on managing the data that really moves the needle in terms of delivering personalized experiences and improving customer satisfaction, they need a data strategy.

Data strategy must be aligned to business strategy. So, foundation question would be “what is the retailer trying to achieve and how can data help to get there?” Answering it will bring clarity on how retail brands want to use data in practice, which will lead to top data priorities and finally a detailed plan to source, store and analyze it.


There are many objectives for organizations to use data and knowing the customer is one of them.


Getting started: Leveraging currently available customer data


Back to customer knowledge, any retailer has already many ways of collecting data with no need of (fancy) major investments that can help understanding and setting reliable customer profiles and journeys. Taking the most and finding the limits of the currently available data should be the first step of any strategy. See below the most relevant:

  • Purchase transactions. By managing POS data, from any channel, retailers are able to segment transactions per spend level, product mix, brand & attribute preference, peak of sales intensity.

  • Loyalty programs – Sign up data. Asking key info for customer knowledge when signing up for a loyalty program (or solely in exchange of a discount coupon) is the easiest way to enrich the transactional data as it gives a new level of analysis.

  • Loyalty programs – Rewards. Regardless of the loyalty program structure, rewards are a very good way not only to increase customer engagement but also to have deeper insights on specific customer topics to refine engagement tactics.

  • Web analytics. Retailers can build contents of any kind (from info to entertainment) in their websites and monitor interactions with visitors to extract helpful information about preferences.

  • Social media impact. Similar to web analytics, if retailers are active on platforms as Instagram or Facebook, they can measure the response to planned impacts to increase customer knowledge accuracy.

  • Customer surveys. Somehow back to basics. Either sending massive online surveys or conducting focus groups with properly chosen participants can deliver significant value to know what customers’ do’s and don’ts.

Needless to say that retailers need a minimum data management and analytics setup to transform data to customer insights.


Making the customer open to share info


Apart from purchase transactions, rest of data is highly dependent on customer’s willingness to share personal information. Therefore, gathering customer data is sometimes a difficult challenge. Main reasons why customers are reluctant to share personal info are trust, difficulty to fill forms and, above all, they don’t see the benefits of sharing data.


So, when planning to capture customer data you must make sure to…

  • Create trust. Communicate the purpose of the information request and show it will be beneficial for better serving the customer.

  • Make it easy. Find right moment and the right way to ask the customer, as well as make the minimum and appropriate questions to meet a current purpose.

  • Spur to share. Define incentives to provoke an impulse to share the requested information, from special discounts to access to exclusive services.

Conclusion


Retail is about serving your customer. To deliver a proper, personalized service at scale you definitely need to know your customer. And knowing your customer requires data. But data is not the final objective by itself: data is the way to better serve your customer. So, when engaging in an initiative to gain further insights about the customer, make sure there is consensus across the whole organization on an approach that balances the effort to capture data with its return on customer satisfaction. And consider that, in all cases, the first step is making the most of the currently available data.

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