By some estimates, 2.5 quintillion bytes of data is created every day. From downloads to shopping cart abandonment rates, customer data is everywhere. More and more, the question that companies are struggling to answer isn’t “what data?” Instead, they’re preoccupied with how to transform big data into big ideas.
Innovating means developing a new idea, product, or method — with the emphasis on “new.” Over the years, many innovations have come about as a result of massive investments in R&D, public sector research, or plain old luck. However, this often takes years to materialize. In the meantime, your customers are becoming more discerning and their expectations are higher than ever before.
Caught in this perfect storm, companies now have to quickly, seamlessly, and consistently deliver value to their customers just to keep up with their ever-changing preferences. And the best way to lead the pack in terms of digital innovation is through small bets that have the potential to deliver meaningful results. Think of it like a boxing match: Your goal is to get in as many punches as possible, rather than waiting for that one knockout blow.
Putting data-derived insight to work
To get started, let’s ask some critical questions: What are the most important goals for your digital initiative? Growing revenues? Delivering a better customer experience? Increasing operational efficiency? Or something else completely?
Unfortunately, many teams can lose direction before they’ve even identified their destination — and the innovation process stalls completely. Teams may have plenty of data and insights, but they aren’t quite sure how to marry customer analytics to the right digital solution.
But don’t lose hope.
To take your digital initiative to the next level, it’s worth going through the exercise of documenting and analyzing your processes for creating a product or service. This makes it easier to identify the pain points. Are customers concerned about the speed of the app? Are there complaints about updates not coming quickly enough?
With this in mind, ask yourself where the value stream breaks down. What is keeping your company from getting new products to market faster, for example? Maybe by looking at time spent on digital projects and how quickly code is being deployed, you’ve discovered that your developers and operations teams aren’t working as collaboratively as they should.
Now that the analytics have helped you find the root cause of the problem, let’s start testing new approaches — but without making huge financial outlays. Testing and failing fast are good things — they help us to learn more quickly. But we don’t have years to test an idea, only to discover that we’ve wasted time and energy. Not only is this the least efficient way to innovate, it’s also the quickest way to destroy any chance of success.
Instead, create an MVP — or minimum viable product — that you can easily test with a subset of your customers. According to Eric Ries, author of The Lean Startup, “A minimum viable product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort.”
For instance, consider using a cross-functional team to create and launch a microsite rather than a completely new app. Try Google App Engine for creating a prototype in no time at all. Or use any number of open source solutions at your disposal rather than spending significant sums purchasing new solutions.
A case for innovating with small bets: Improving the retail experience
We regularly go through the above process with our clients, and the lessons we learn along the way are invaluable. For instance, during a period of declining sales, one of our clients — a shopping mall developer and manager in South America — wanted to improve the shopping experience at its malls. We examined the pain points and decided that we needed to grow sales by bridging the physical and digital worlds for the younger shopper who has grown up in a digitized world and to win over those shoppers who are beginning to embrace digital.
Next, CI&T created a system that generated customized and geolocalized promotions, which was built on Google Cloud along with numerous analytic and Big Data solutions. Based on customers’ interactions within the mall’s wi-fi system, retailers were able to offer targeted promotions via text. Let’s say a customer walked into an appliance store, they might receive a discount coupon; if they went to the movies, they might get an offer for dinner at one of the mall’s restaurants; if they had been looking for smartphone deals lately, they may be invited to a product launch.
A pilot was conducted at four network malls during Black Friday. For one week, discount coupon campaigns were run for about 3,000 potential consumers. The success was higher than expected — clickthrough rates averaged 30%, and customer receptivity and feedback with the campaign were excellent. For example, in one shoe store chain, the promotion needed to be closed in just two hours because it had achieved 100% conversion of their vouchers. In a movie theater chain, the Black Friday coupons sold out in 36 hours.
With this type of evidence in hand, it was time to test this solution during the most important season for retailers — Christmas. The Christmas campaign affected about 30% of the customers that circulated through the malls and realized a 40% coupon redemption rate.
No matter your role, you need to become a data scientist
The above case helps illustrate what can happen when you responsibly leverage the insights about customers that are at your disposal. But technology executives also need to take on a new core competency — namely, thinking of themselves as data scientists. According to KMPG’s recent study of consumer goods and retail CEOs, 68% of CEOs in the U.S. are not leveraging digital tools to connect with their customers as well as they should. But to come up with digital solutions that deliver meaningful value to customers, leaders need to understand what data they have, what it means, and how they can use it to test new ideas.
Further, since organizational silos are breaking down and teams are becoming more cross-functional, it’s all the more important to use your data set as a basis for decision making and measuring success. It’s also crucial to consider what can be learned about customers through social media channels, Google Analytics, mobile app ratings, product reviews, transactions, server logs, and more. The innovation process should look to uncover what the data are telling us about our customers.
Making [small] bets on the future
What you do today is a down payment on tomorrow. And by continually testing your assumptions and data, you’ll invariably unearth new discoveries about your customers that allow you to reshape the customer experience — bit-by-bit and test-by-test.