Most retailers have made major investments in their marketing tools, their data, and their teams.
They are aggregating customer information in data warehouses and making it accessible to marketing execution systems. New data science groups are being formed, and CRM teams are being integrated within the broader organization. So why is it still so hard to solve customer centric-challenges like the one time buyer problem? To reduce churn without over-spending on promotions? To focus your ad spend on consumers predicted to become high life time value customers?
In many ways, the last ten years have been the Age of the Channel. Email, Display, Search, Social, eCommerce -, the list goes on and on. Each channel has a dedicated team, dedicated tools, and dedicated analytic systems. But according to Forrester, we are now entering the Age of the Customer. And that requires customer-centric tools, customer-centric data, and customer-centric analytics that cut across channels. With all the investment in teams, data and technology, there is still a huge void right in the middle of the modern marketing technology stack that makes it nearly impossible to understand individual customer needs and wants. Without this deep understanding, brands will not be able to tackle customer-centric challenges like the one time buyer problem. Or reduce churn without overspending on promotions. Or efficiently acquire new high value customers.
The gap in the retail marketing technology stack sits between customer data and the marketing teams themselves. Even after consolidating customer data in data warehouses, marketers still aren’t any better equipped to tackle difficult customer-centric challenges. The gap is felt in two ways - through a lack of actionable insights generated from the data, and in the inability for marketers to easily access the insights to improve the day-to -day performance of the marketing team.
The 3 steps to generating data-driven customer insights
The explosion of channels and touch-points and all the interactions taking place across them is generating tons of data. Yet rather than feel empowered by all of this data, today’s marketer feels overwhelmed. Everyone on the marketing team must have easy access to cross-channel customer behaviors and insights in order to find and exploit profitable growth opportunities. There are three steps to generating these types of insights.
- Consolidate data The first step is consolidating and organizing data, and there are many ways to do this. Online retailers generally have a user ID that tracks purchases over time; they can then layer in engagement metrics on top of this data. Omnichannel retailers have also made great strides in consolidating their data, stitching online and offline data together to create a more complete view of the customer. Note that this does not need to be a perfect 360 degree customer view to be useful - great results can be delivered with relatively small amounts of data (see the Aldo case study below).
- Surface historical and predictive insights Once the data is consolidated, the next step is generating insights. Advances in machine learning and predictive analytics have revolutionized the ability of marketers to uncover hidden insights that can translate into massive revenue opportunities. It is not enough to simply look at past purchase behavior it is important to also anticipate their future needs. With the analytic tools available today marketers can get instant access to cross-channel behavioral data as well as accurate predictions about each customer’s lifetime value, product/category/brand affinities, likelihood to churn, responsiveness to pricing and promotion, the list goes on and on.
- Update customer scores daily Finally, even the companies that have awesome analytics teams powering insights, the insights aren’t necessarily refreshed in an ongoing manner. If product preference scores are only updated once every month and you need a list of fans today, the value of the insights are greatly reduced.
The 3 steps to activating insights to improve marketing performance
"Knowing is not enough. We must act." Johan Wolfgang von Goethe
1. Put the insights in the hands of the marketing team It is not easy for marketers today to get real-time access to data-driven insights. Generally marketing teams need to file tickets and wait days or weeks to get a segment created or a predictive model updated. A marketer-friendly interface is crucial - the marketer needs to be able to access customer driven insights at the speed of decision.
2. Integrate insights with the marketing execution stack Creating an audience segment and porting that segment to an email service provider or a social media advertising platform needs to be as simple as pushing a button. This is easier said than done when some of the tools are keying on an email address, some on a CRM ID, and some on a CID. But it should be simple for the user to export audiences into existing workflows for channel managers.
3. Use incremental measurement Finally, a customer-obsessed organization needs to measure success on a customer level. You can’t just measure the performance of your email or your social campaign in a siloed channel. You need to understand the impact these campaigns have on the lifetime value of each customer. To solve this problem, retailers need a system that makes it easy to test and measure the incremental impact of campaigns across channels. A system that automatically creates control groups, and provides statistically valid measurement and insights into the impact on each audience segment included in the campaign. Marketers need to be empowered to get real-time performance feedback themselves.
Customer Analytics in Action
A great example of a company that is in the process of moving from traditional brand messaging to a customer-centric, data-driven approach is the fashion retailer Aldo. Ian Richards, the Vice President of CRM and Analytics at Aldo, has been doing an outstanding job of driving transformation in the organization.
Move from "spray and pray" to customer centricity Aldo has been a leader in fashion retail since 1972 with a very loyal customer following. But the focus had traditionally been on product over customer, with little to no segmentation in their direct marketing, gaps in their data, and a “spray and pray” campaign approach. The CRM team was established to drive a more customer-centric approach to marketing and to leverage customer data and insights to drive transformative improvements in marketing effectiveness.
Use analytics, segmentation and modeling to drive personalization and engagement The Aldo team started with what they had. They pulled together small data - engagement metrics, moments of interaction, and purchase history - into a more complete view of the customer. They utilized analytics, segmentation, and advanced modeling to guide their customer campaigns. Through these insights they uncovered high value segments and the next best offers for those segments. They were also able to identify high value shoppers and create an A-List loyalty program with offers perks such as early looks at sales and special previews of new products for high affinity customers.
Email revenue increased 70% As a result of the increased personalization and improved relevancy, email campaign performance improved dramatically. Open rates increased by 60%, conversion rates improved by 130%, and email revenue was up a whopping 70%.
Take the Quiz
How ready are you to make the transformation from channel-centric to data-driven, customer-centric marketing? Take our short quiz and find out.
- Is your customer data in one place?
- Is this data hub integrated with your marketing stack?
- Are you measuring success using control groups and incrementality?
- As a marketer, are you able to access customer data in real time?
- Are you using predictive analytics?
- Are your customer scores updated daily?
If you answered no to any of these questions, please reach out to us. We can help you use the data you have to dramatically improve your marketing performance at every stage of the customer lifecycle.
About Custora Custora is the leader in advanced customer analytics for retail. Our Customer Intelligence Platform uses machine learning to identify profitable growth opportunities based on analyzing and predicting the behavior of each individual customer, and gives marketers the power to activate these insights on-demand.
Brands using Custora's predictive analytics dramatically improve their marketing effectiveness at every stage of the customer lifecycle. Churn prevention programs perform better because marketers can Identify the exact moment that a customer is about to leave the brand. Broadcast emails have higher conversion rates because they are personalized to the needs and affinities of each customer. New customer acquisition delivers a greater ROA because marketers can predict which prospects will be high CLV customers. One time buyers can be driven to make a second purchase, and repeat purchasers can be transformed into loyal customers.