A marketing program is only as good as the data that feeds it. Here’s how to optimize data use.
In its June 2017 State of Marketing Report, Salesforce.com found that 57 percent of marketing leaders now believe artificial intelligence (AI) is “absolutely” or “very” essential in helping them provide personalized experiences for their customers. Even more (64 percent) say their company has had to redouble its efforts to deliver a consistent experience on every channel, just to keep up with customer expectations. AI and omnichannel capabilities have crossed the chasm in retail. No longer are they nice to have; they are must-haves.
But among the hype of new technology adoption, it is extremely easy to lose sight of the critical issue that’s been there all along. The output from these kinds of initiatives will only ever be as good as the data that feeds them.
In the end, data’s primary job is to help brands acquire new customers, retain existing customers, and grow revenue. It is there to help marketers intuit for themselves how to interact with customers more effectively. But how do you know the data you’re using is telling you the right things and thus fueling revenue objectives? And how can you use data to make truly emotional connections with customers and drive their satisfaction?
Here are three simple ways for marketers to confirm they are using data as effectively as possible.
Quit relying on third-party data services.
There’s a lot of money spent on third-party research data, ostensibly to help marketers understand how their buyers think. The question that so often gets swept under the rug, however, is this: “If these aren’t our customers, how well is this data going to tell us what our customers think about us? Is it really going to inform us how our customers will respond to our attempts to connect?”
The challenge with third-party data is that you don’t know where it’s coming from, how it was collected, and whether it’s up to date. If the generally accepted drive in retail marketing is to mimic the success of Amazon, ask yourself this: If we want to compete with Amazon, shouldn’t we prioritize getting better data about our own customers?
With data quality management, get preventive.
Data decays faster than you think. A 2015 study from Acuate found consumer data decays at a rate of about 10 percent per year. Someone gets married (and changes their name) every 12 seconds. More than 6,000 people die each hour. Eleven percent of the population changes its address every year. This only fuels the likelihood that you either won’t be able to reach your prospects, or their shifting needs, and priorities will create a void of interest for your products. If you are marketing without a consistent, regular data health check process, you run the risk of missing the mark on messaging and even alienating current and potential customers.
There are several core components of a proper data cleansing regimen:
1. Always verify customer contact information before data reaches the customer database. This can be accomplished with a live, real-time validation step behind the web forms, CRM fields, and customer service screens that customers, sales executives, and contact center agents are tasked to fill out every day.
2. Establish contact data health checks as a routine part of marketing operations. There are many software tools available to handle this task. The key, though, is to work within a duplicate copy of your production data set because you need to allow for mistakes to be made during the cleaning process. Name the duplicate database something like “Cleaning in Progress,” and when the process is completed, publish the clean data back to the applications and systems that use it.
Believe it or not, this is one of the most important steps in data cleaning but is often overlooked.
3. You can also choose to sync verified, clean data across systems—using no-code data integration tools, data quality tools, master data management, or API synchronization.
Thankfully, preventive maintenance is fairly straightforward, and even if errors slip through, fixing them has become easy, too.
Skew data collection priorities toward personalized experiences.
The Boston Consulting Group (BCG) reports that brands that create personalized experiences with their own proprietary data see revenues increase two to three times faster than those that don’t. However, the report finds that only 15 percent of companies are “true personalization leaders,” so there’s still significant opportunity to grab onto this process as a competitive differentiator.
Typically, the most effective personalization initiatives spin out of factors such as loyalty program status, browsing behavior, purchase history, and cart abandonment. For example, in the post-flash-sale reality, Gilt now connects with its shoppers through customized emails with information from their recent searches and abandoned shopping carts.
For data-driven marketers, it is much more important to be focused on the right data versus more data via tools like a customer data platform. Trying to make decisions based on every piece of information out there is a recipe for, at best, paralysis. At worst, it can lead to brand dilution.
By Paul Mandeville, chief product officer at QuickPivot