You don’t want to end up buried by a gigantic pile of irrelevant, erroneous, and outdated customer data that feels like it has a mind of its own.
When you keep a tidy house, you always know where to find everything, and almost anyone that visits can find their way around as well. Conversely, if you’ve got a haphazard way of organizing, you may well know where to find things, but it will be close to impossible for anyone else to find their way. The same goes for your customer experience (CX) data: you can only trust the information in your CRM if everything is tidy and organized.
Did you know that over 50% of businesses spend more time cleaning up and tidying their data than actually using it? Imagine what they could be doing with all of that time. We know that better data creates better user experiences, and because of that, many CX teams are hungry for it. Unfortunately, though, no matter how much data you have, it won’t help deliver customer value, increase marketing efficiency, or do much of anything if it’s incorrect, incomplete, or messy.
In this blog, we’ll break down five ways that you can impact the cleanliness of your CX data right now. From steps to take before conceptualizing your CRM structure, and how to right the ship if you’re already far into your data journey. Don’t worry; we’ve got your back.
Consider what you need
If you don’t already have a CRM filled with data, you can make a huge impact right from the start. The most successful data strategies match the company’s needs. As you start to conceptualize what you want your data to look like, think about your future goals, and who will be using this data moving forward. For instance, if you’re building your data structure primarily for your customer success team to use, you’ll include different information than you would for your marketing team.
That said, don’t go overboard. Less is more: you don’t need to have everything mapped and set up from the start. Begin by defining the bare minimum: rules for formatting data, how you’d like to treat standard abbreviations, and how you’d like to treat common titles or proper nouns. Not sure what we mean? Think about all of the different ways to write a phone number:
Start with a clear set of rules for how you want these things to look, and you’ll save yourself heaps of trouble in the future.
If you already have rules in place and have a data ecosystem up and running, revisit them regularly, and look for opportunities to optimize or automate. About 30% of company data becomes outdated every year. Companies change, and your data will change with it. Traditionally, companies only pay attention to data cleaning when they are migrating or first implementing their CRM. You don’t have to wait that long! Check-in with your data processes regularly and save yourself and your customers time and pain.
Audit your data
According to Health Data Management, close to 90% of data management professionals say they’ve added bad data to their data stores. The chances are high that someone at some point has added some incorrect or improperly formatted data to your CRM. Rather than explicitly tracking down the wrong-doer, instead, create a process to start catching the issues as they happen or stopping them before they occur.
For example, imagine that your customer success team regularly noticed that records updated by the sales team had incorrect or incomplete information. After all, eConsultancy studied the most common ways companies collect data, and a human being manually enters most data. The only problem with human beings is that we make mistakes—so, of course, your sales team, even being the rockstars that they are, will make them.
When you find the issue causing your data problems, take action to correct it moving forward. In the above case of your sales team improperly entering information, for instance, you may consider introducing training or incentives based on data quality or even finding software to help reduce input errors.
Link your data
There is software for everything nowadays. Unsurprisingly, 92% of businesses report having 16-20 unique data sources, with data spread across multiple locations in multiple formats.
Instead of keeping siloed data, spread out across the ether without any impact, link it together, and cross-reference it. The more places you are pulling information from, the more vital the information at your fingertips will be. You will be able to paint a much more clear picture of the customer if you are pulling information about them from multiple sources.
To keep it all organized, create a single customer identifier, such as a user ID or email address, associated with their records across different platforms. These IDs will keep everything tidy and make it easy to set up additional data points to feed into your future strategy.
Fix your data
According to a 2016 HBR article, flawed data costs businesses in the U.S.alone roughly $3B per year. Fix your broken, imperfect data by going through and cleaning, appending, and deduping it.
Using your guidelines from the consideration period at the beginning of this blog, go through and ensure that all data pieces are following the same structure.
Verify that things like addresses, company names, and data are all written in the same way. Ensure that you’re using, for example, Corporation versus Corp. across the board, or that your date formatting is always the same. Machine learning and NLP are excellent tools to use during this process. They make processes like deduplicating or detecting human error straightforward. Beyond that: they help you identify contacts who haven’t engaged with your content for a long time and remove them from your CRM.
You wouldn’t keep a text message from someone that you went on one date with twenty years ago, why would you do the same equivalent with a customer?
Lastly, if you are using a third-party tool to augment or add color to your data, ensure that they are reputable and that you have a way of removing what it adds if it doesn’t work out.
Train your team
People in customer-facing roles waste 50% of their time hunting for data, finding and correcting errors, and searching to confirm data sources that they don’t inherently trust. Conversely, data analysts and people that are well-versed in setting up processes and systems don’t have the same difficulty. Ironically, though, it’s the former demographic, rather than the latter, primarily working with data after it’s set up.
Give your team the tools that they need to be successful, and train them on the best ways to manipulate and utilize data. With a little bit of data wizardry under their belt, they’ll be much quicker with auditing and updating data, and they’ll have more insight into what they need from a data strategy.
Codecademy has free data science courses that your team can take to level up their chops if your team doesn’t have internal resources that you can use to train them.
Don’t get buried
The best way to get clean CX data right now is to implement plans for the future. You don’t want your team to be buried by a gigantic pile of irrelevant, erroneous, and outdated customer data that feels like it has a mind of its own.
Take time to understand what you’re looking for, whether that means from the start entirely, or from this point moving forward. Then, take time to make sure your data is relevant and meaningful by linking your various data points together and putting in the effort to dedupe and clean up the information you currently have.
Lastly, empower your team. If you train them now, you’ll have less work in the long run to keep all of your data squeaky clean and useful across the board.