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Customer data integration needs strategy to derive meaning
Your approach to customer data integration needs to be guided by another strategy to determine which information is relevant.
Over the past few years, customers have become more empowered than ever. The pace of technology change and our expectations for instant gratification in this highly connected and mobile world are mostly to blame. But successful companies are trying to take the guesswork and inefficiencies out of the daily execution of customer-facing activities, which can have dramatic impact on a company's bottom line.
From a customer relationship management (CRM) perspective, we need to understand how to engage customers and identify their needs based on all the experiences they may have with your company. Anecdotally, we often hear that employees and customers get frustrated when the "live" experience doesn't match with their perceived standards or customer experience in other channels. So, for example, a customer buys something online and can see the information related to the order online, but a customer service representative can't access that same account information when on the phone with a customer.
One challenging aspect of CRM a decade ago was the siloed approach of many implementations. Business leaders quickly saw that to be effective in sales or service, information would be important. What we did not see in practice was that we really need a methodical approach in order to be effective today. Face it, whether we're a small or medium-sized or a Fortune 500 company, we all have more data than we can consume to do our jobs.
Identifying relevant information
Before we can make decisions about integration, we need to determine which information is relevant and how that information should be available. The process takes extra time, but we continue to see the pitfalls of companies that concentrate more on the volume of information rather than on its relevance and on how it should be presented.
Next, the speed at which you deliver information to those who need it is as paramount as the context in which it is presented. We see many requests to present high volumes of transactional data where speed becomes an issue because of the difficulty in sorting it and getting to the critical information. The lessons learned here are that data has a shelf life and that less is more. This is why the customer lens can be enlightening.
With the right CRM data integration strategy, you can connect the dots from the marketing, sales and service touch points. The historical challenges with effective marketing and validation through the sales organization are now being addressed through marketing automation. The ability to turn what we already know about a customer into meaningful messaging creates velocity in sales and a happier customer.
The same is true of linking transactional results and service information. Data integration gives answers to questions that have not been asked before. It enables us to validate how and why customer satisfaction is on the rise -- or suffering because of changes in business processes, manufacturing or customer policies. Companies may change good processes because of poor customer experience, even though a change in policy may be misguided. So, for example, a customer may have ordered two products and had a problem with only one, yet he may still be required to return both to have his problem addressed.
We should embrace the obligation to incorporate data with interactions and process. In doing so, we also need to understand how today's consumer and our employees see the world. We live in a sound-bite world running at the speed of the Internet. We are always on and become dissatisfied when our perception of simple tasks become difficult or time-consuming.
So, bringing the discussion full circle, there are several takeaways that you should incorporate as the foundation for enhancing customer experience with more meaningful data.
Go to strategy first before considering technology. Map the data into consumable information based on relevant data. And learn through constant feedback and expect change rather than reacting to it.
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