Not to sound too alarmist, but retail is evolving faster than ever before. Brands that don’t adapt – and marketers who don’t adopt new technological skills – will not last. But all hope is not lost.
Kate Whittington, a consultant in McKinsey’s Customer Analytics practice and the former head of customer insights for Target, recently joined us for our Customer Analytics for Retail Marketers (CARMA) conference to share her thoughts on the current changes in retail and the importance of overcoming the challenges by building a modern customer data analytics strategy.
What’s Fueling the Change?
McKinsey identified six macro trends behind the fast-moving changes in the retail industry.
- Real-time relevance. New data sources and analytical techniques have enabled companies to deliver increasingly personalized consumer journeys in real time, based on past behavior and predicted future needs.
- Next-generation digital experiences. Consumer journeys are being redesigned with the help of new technologies, particularly those for social and mobile, that are improving how companies interact with consumers.
- Mobile first. In light of growing consumer preferences for mobile, companies are raising the bar on mobile research and shopping experiences.
- Omnichannel innovation. To meet consumer demand, companies are radically evolving to deliver seamless, cross-channel experiences. Pure play ecommerce retailers are opening physical stores, and traditional brick-and-mortar brands are experimenting with new inventory-light, experience-heavy formats.
- Business model innovation. Enabled by digital, companies like Uber and Amazon are expanding into new products and services, disrupting industries and establishing new revenue streams.
- You-inspired. In a world where every consumer can also be a publisher, companies are leveraging social platforms in more sophisticated ways to drive engagement, sales and loyalty.
Retail Industry Reaction
As a whole, the retail industry is not reacting well to the customer-driven changes in their business. A recent McKinsey analysis shows that while over 55% of the industry’s revenue is influenced by consumer digital interactions, only 18% of the industry’s technology investments is going toward crafting and improving new retail models.
To engage and delight customers effectively, the marketing department must have firsthand access to customer data. So take control of it. Marketing, not IT, must own the customer data strategy and the analytics used to glean insights from it. Waiting for a service organization to provide answers to marketing questions stifles the creative process and creates a barrier to moving at the speed required to succeed today.
Building a customer data strategy requires three key steps.
Step One: Understand the types of data you can collect.
In the good old days, retailers collected purchase and loyalty data and housed it in their CRM system. It was pretty simple for marketers to understand – even if it wasn’t that easy to access.
Today, customer data comes in two forms: internal and external. Internal data is the data you capture directly from your customers. Think product information, POS, website, mobile and any number of data points throughout the customer journey. External data is collected from third-party sources, and the type of information available seems to change almost daily. It includes social media activity, location data, web behavioral data, photos, even data gathered via embedded sensors or exercise logs.
Step Two: Identify the questions you want to answer.
Think about your customer data in terms of four categories.
Transaction History includes all the purchase data you have captured and stored. From this information, you would expect to answer these types of questions:
- Who are our most valuable shoppers?
- What do they purchase? Are their purchase patterns unique to your best customers?
- What is the average order value? Is it growing or shrinking?
Demographic and Geographic Data includes things like address, gender, ethnicity, family status and income levels. This data can be gathered from customers but is often purchased from large credit rating agencies like Experian and Epsilon. From this data, you would expect to answer questions like these:
- Where do our fashion-conscious customers tend to cluster? What’s their typical family size, education level, and socioeconomic status?
- What’s the average age of customers who buy accessories vs. outerwear?
- Are there pockets of high-value customers in locations where we don’t yet have a store?
Preferences and Attitudes refers to web browsing history, survey information and overall engagement. From this data, you would expect to answer questions such as:
- What are the most important purchase triggers for our special occasion shoppers?
- What do full-price shoppers value about our brand?
- What issues (fit, fulfillment, product quality) cause first-time customers not to return?
Social Network and Context Data is some of the newest data being used by marketers today and includes product ratings, social media posts and interactions, and mobile-phone powered location data. From this information, you would expect to answer these kinds of questions:
- Who are the influencers within our customer base?
- What other brands are our customers engaging with?
- What types of content/experiences drive customer advocacy?
Step Three: Align Your Data and Analytics Strategies.
Customer analytics is the process by which you turn data into insights – and insights into action. To be effective, you must create a long-term vision for how your analytics will influence your customer interactions now and in the future. You should also align your analytics with your broader strategic priorities, and prioritize speed over perfection. Be case-driven, and start small and focused. Strive for a relevant, valuable application of your analytics.
In many ways, your use cases will define your data strategy – the information you need to collect and the speed with which it needs to be updated and made available. Some high-impact marketing and personalization use cases include:
- using customer analytics to create differentiated loyalty programs.
- using predicted product affinities to power digital personalization.
- identifying potential high-value customers and optimizing their engagement early in their life with a brand.
- tracking customers at risk of churn and providing individualized messaging to get them back on track.
As data-driven marketers, you are in a privileged position to drive the analytics at your company. Own your customer data, take the time to gain a deep understanding of the power of analytics, and apply your test-and-learn mindset to ensure your success.