It’s one thing to want to please customers. It’s a whole other thing to actually deliver a consistent experience that satisfies customers and keeps them coming back for more.
Thankfully, the delivery of customer experience (CX) no longer relies on outdated, often generalized perceptions of what it takes to give customers what they want. With consumers leaving thick bread crumb trails on their digital devices, organizations can capture real-time, irrefutable insight on what they’re thinking and how they’re behaving.
Consider consumer data as evidence and analytics as the crack detective that pieces together all of the clues. That’s the thing about data: Consumers generate so much of it that there is no way a company can make sense of it all without help. That’s where the detective – the analytics component – should be called into duty to sort all of the information and then break down the data into small groups that reveal consumer history, sentiment and a host of other detailed insights.
But you can’t just aim a data analytics spotlight into the night sky and expect to instantly master CX. Data analytics acts like a superhero only when an organization has first devised a strategy on how it will use it. Here are three things to consider when devising your strategy for managing customer data and analytics.
Which data matters?
Just because you have a lot of data doesn’t mean you have to use it all. Before embarking on your renewed (or perhaps new) data analytics campaign, sit down with stakeholders to determine which customer data will build the strongest customer profiles. Too much data, especially if it’s unnecessary, will only slow the process.
Narrow your list to data that will reveal who your customers are (for B2C, it would include age, gender, relationship and family status, geography and other identifying information) how they spend (purchase histories, reaction to past campaigns) and why they spend (new home, new child). Also figure out if the data will help map your customer journeys; if it’s tangential to the process, maybe it can be put aside. Lastly, connect the insight to the particular processes that are integral to shaping customer experience. Prioritize data that lets employees personalize relationships and take actions that will enhance CX.
Who is in charge of data analytics?
Most employees will benefit from having insight on customers, but someone needs to lead the charge. If possible, have a chief CX data analytics officer. If budget precludes the creation of this position, at least have someone who can handle most of the job responsibilities.
In short, those duties call for someone to look at the analytics process and determine whether the right questions are being asked to arrive at the right answers. This is the lead person who helps determine which data matters. For example, a data officer can see more quickly than others that a particular type of data isn’t best informing analytics efforts. The officer can see if the data source itself is flawed or if it’s more a matter of tweaking how customers interact with the organization’s data collection initiatives.
Just as importantly, a data officer can lead the analytics fight up the hill. Departments might see the value of analytics but inevitably be consumed by their direct responsibilities. A data officer can stay on top of all analytics initiatives and shake the tree when needed.
Which technology will help?
Human efforts will only go so far in making sense of your customer data. Ultimately, technology will be the difference maker. Here’s the detective of customer data that, when implemented as part of a sound analytics strategy, is also a superhero. Stand in wonder of the customer data platform (CDP), especially one that uses AI as its superpower.
An AI-supported CDP uses a data layer to identify, unify, cleanse, and enrich your customers' personal, behavioral and transactional data. For instance, take Zylotech's CDP. Its data engine pulls all of your customer information – whether it's spread across email, social posts and text messages, or if it's buried in silos – and then standardizes all that data, removing duplicate information and enhancing it.
The analytics layer then takes all the data and builds customer profiles that offer rich behavioral insights on customers. You’ll see who's about to buy and why, and can also pinpoint who's about to leave your marketing funnel. The data shows what course of action you should take: up-sell, cross-sell or promote something else.
Gartner recently found that 75 percent of surveyed organizations increased their CX technology investments in 2018. If there was ever a time to make the right investment to advance your CX efforts, it’s now, 2019. With the right customer data strategy in place, a CDP will put you closer to customers than ever before.
Iqbal Kaur leads customer success & product management at Zylotech. When she’s not thinking of new AutoML models or conceptualizing product UI, she spends her time hiking in the Nepal Himalayas. An avid fitness enthusiast, she loves to challenge herself with the crazy workouts from the Insanity series.
If you liked this post, check out our recent blog post: What happens with companies that ignore customer experience.