Retail’s Data Conundrum: What Compels Consumers to Share Personal Information?

Data analytics has proven to be an important way for retailers to glean insights into the patterns, preferences and behaviors of customers. However, getting the right customer data remains a challenge. Consumers can be hesitant to share personal information, especially when the benefits of doing so aren’t clear. They're willing to make an exchange though if you can demonstrate how that exchange directly benefits them.

Netflix is a prime example of the potential of data sharing. Every time a consumer watches a show or leaves a rating, they're essentially sharing their data with Netflix. But the benefit is easy to see: better, more relevant recommendations. In the world of retail, the benefits of data sharing aren’t always quite so clear for consumers. In fact, in a recent survey of more than 1,000 U.S. consumers, [24]7 found a significant number were looking for promotions and discounts (43 percent) and expedited customer service (39 percent) in return for sharing personal data.

The survey results suggest that consumers are willing to share data — for a price. The challenge for retailers is to ensure they're getting the most from the data by leveraging it to understand consumer behaviors while enhancing the overall customer experience. That experience should be a key consideration when creating loyalty programs and developing longer-term revenue opportunities.

The best way to approach creating a great customer experience is to start with the data you already have, and use it to understand consumer intent. For example, if a customer calls with an issue, the agent who handles that call should have easy access to the customer’s purchase history or key identifying information to ensure speedy and accurate resolution. The agent should never have to ask the customer to repeat information that the company already has. Similarly, if a customer is calling about an item that’s out of stock online, the agent should be able to see what the person was trying to do, and offer proactive recommendations. For example, if the agent has information about the consumer’s location, the agent can suggest retail stores in the area that may have inventory of the product the caller is looking for.

When done right, personalization can be a highly effective way to forge trust and loyalty with consumers. Relevancy is one of the more important factors shaping consumer perceptions of personalized marketing messages, such as online ads and emails. According to the survey, 26 percent of consumers cited relevance as the main reason they appreciate personalized marketing messages. However, a similar number of respondents noted that irrelevant messages — i.e., those which say you don’t know me or my needs — are what bother them the most. If you go down this path, you're on course to discourage customers and lose confidence in your brand. The cost of getting this wrong even once could mean some customers never return.

The survey also suggested retailers aren’t making the best use of the data they already have to enhance the customer experience for current and future shoppers. As such, nearly half of respondents (47 percent) said they're doubtful sharing more data would improve their overall experience. Today's technology allows for access to vast amounts of data about customers, but not all companies are putting this to use in a way that drives customer loyalty. With big data, machine learning and artificial intelligence (AI) becoming more commonplace, retailers can now drill down on customers’ personas, engagement behaviors and preferences — all of which help ensure relevant messaging.

Personalization and customer experience are continuing to evolve from reactive to proactive. Rather than analyzing data to simply understand how consumers have behaved in the past, companies can now focus on prediction to better determine customer intent — i.e., what is this customer trying to accomplish? To be truly successful, this needs to happen in real time and across any channel. Some leading retail brands are already using data to accurately predict consumer behavior and to improve customer experience. And as machine learning and AI technologies become even more commonplace, the most successful companies will be those that tap into the power of these tools to enhance their customer acquisition and customer engagement efforts across all touchpoints.

Scott Horn is the chief marketing officer of [24]7, an AI-driven customer experience software and services company redefining the way companies interact with consumers.