Have you ever had a conversation about a product or service with a friend, and a few hours later, an ad for it eerily pops up on your social feed despite never having searched for it earlier?
Chances are, you might have experienced this phenomenon that has led numerous people to believe in the conspiracy that Facebook passively listens to their phones’ microphones. In fact, the company had to make an official statement to clarify that they don’t, in fact, listen to your private conversations.
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Implementing Data-Driven Decision-Making at Scale
So, how did they get so good at knowing what ads to show you? The answer lies in their granular data analysis of your and your friends’ likes and preferences. This allows them to make educated guesses on the relevancy of certain ads based on your recent interactions with them. Many sophisticated data about you and your network allows digital native companies like Facebook to know when, where, and how to show you the most relevant ads suited to your profile.
It’s not just about the collection of data but also the ability of these companies to synthesize meaningful user insights in real-time; and make the entire decision-making apparatus, both strategic and operational, run on data-driven insights. That’s how they can implement such data-driven decisions rapidly at scale.
These organizations live and breathe data. Data is not just a necessity but the lifeblood of these digital natives.
Today, digital natives are the most valuable companies in the world. One of the main reasons for this is their fundamentally different approach to data and analytics. Many of the techniques they utilize in this approach fall broadly under the concept of Product Analytics.
Challenges Faced by Retail Stores in Building Data Capabilities
Let’s now run a thought experiment with retail stores. Have you ever entered an electronics store and seen an offer specifically for a laptop model you plan to buy? Or visited a grocery store to buy soft drinks and some tissues, only to find a deal for this exact combination just when you enter?
Probably not. This is because large retail companies have nowhere near the level of business insights about their customers that digital natives do. There are multiple reasons behind it:
- Information about their consumers is limited: Brick-and-mortar companies do not have data-capturing mechanisms that are robust enough to build strong user profiles of consumers.
- Consumer Data is stored in silos: Even if the data is captured, it may reside in various siloed sources without a standardized method for integration.
- Evolving Products/Offerings to suit changing customer needs takes time: Even if the organization can gain insights from data, the product development cycle is too long to offer any advantage over the competition.
- There is cultural resistance toward data-driven decision-making: Low data literacy and a tendency to follow time-tested ‘rules-of-thumb’ within the organization make it difficult to build a data-driven culture in retail companies.
Learnings from Digital Native Companies
To tackle these challenges, here are 4 lessons from Digital Native companies that can help retail companies derive business value from data:
Incorporate Data into the Core Strategy, Vision & Leadership of the Company
Executive leadership needs to recognize the importance of data-driven decision-making and make it an integral part of their vision. For example, HUL, an Indian FMCG company, created a digital strategy driven by CEO Sanjiv Mehta called “Reimagine HUL,” which placed data & analytics at the heart of the company strategy.
Underpinning this initiative was Mehta’s reshaping of his view of the company’s biggest assets from just “people and brands” to “people, brands, and data.” Since the launch of this initiative, the company’s stock outperformed the Nifty50 – a representative index of the Indian stock market by over 2x.
Build Integrated Consumer Profiles Based on Internal & External Data from Touchpoints
In 2012, Target, a chain of departmental stores in the US, made headlines for sharing coupons for baby items with a teenage shopper, thus, implicitly predicting that she was pregnant even before her own family found out. Such detailed profiling and customization were possible only because the company assigned customer IDs to users. These customer IDs were tied to internal data points, such as their purchase history, and external data points, such as demographic information that they purchased from external sources. Since 2012, Target has consistently outperformed the S&P 500 by over 2.6x.
Incorporate Product Analytics techniques within product development
Digital native companies such as Amazon and Google understand users at a granular level using Product Analytics – a process that closely tracks, visualizes, and analyzes user engagement and behavior data to improve and optimize products.
Many of the techniques, such as A/B Testing and Behavioral Segmentation used in Product Analytics, could be relevant to retail companies as well. This can help reduce product development and concept validation turnaround time from months to days, allowing retail companies to be more agile in delivering products suited to shifting consumer tastes and preferences. Companies such as NielsenIQ provide services that allow for A/B testing of product concepts within 24 hours for any given demographic, allowing for speedier market discovery and product development.
Radiate a Data-Driven Culture Throughout the Organization
The phrase “culture eats strategy for breakfast” may be quite banal today, but it is actually very true in the context of data-driven decision-making at retail companies. Radiating a culture of using data throughout the organization is crucial to ensure that such habits do not end up in silos in different pockets of the organization.
Building robust data literacy programs are a great way to drive retail companies toward this goal. For example, Starbucks introduced a data literacy program that focused on elevating members’ ability to use data and increasing the adoption of existing data tools so that users are given the ability and the nudges to start making decisions with data.
Thus, for retail companies to be as data-savvy and nimble in decision-making as digital natives, they would need to incorporate data into their core strategy, build integrated consumer profiles, adopt Product Analytics concepts within the organization and radiate data-driven culture throughout their organization.
