In a difficult economy, companies must spend money where it will have the greatest impact on revenue. Unfortunately, spending money to better manage data is not always the top priority when it comes to customer relationships. Far from it. Data is often an afterthought when companies face the task of developing, nurturing and creating new connections with customers.

In reality, data management can significantly influence how a customer interacts with your brand, whether you’re a brick-and-mortar retailer selling sporting goods or a financial services company selling securities. Improperly managed data will ultimately affect your relationship with customers – and impact your bottom line.

Customer data is critical to understanding customer preferences and purchase habits, as well as the effectiveness of your marketing efforts. The key to building and maintaining a profitable customer relationship often comes down to how much you know about your customers – and to what degree that knowledge can inform more productive customer interactions.

The Customer Data Challenge

For years, companies have collected more and more information on customers, leading to an explosion in the amount of data available about the customer. Even though more data may seem to be a good thing, the overall complexity of the customer data ecosystem can have a negative effect on customer satisfaction.

A typical tactic for managing customer data is a customer relationship management software deployment. However, many of these systems get derailed by inadequate data quality processes. CRM applications often contain inconsistent, inaccurate or unreliable data because of a lack of standards for customer data across the enterprise. At the same time, the CRM system is rarely the only system of record for customer data. For example, information can be gleaned from different applications that support marketing, sales, finance, distribution, support and call centers.

It is no wonder that the majority of CRM-related projects fail to do what businesses hope they will do. When companies try to get a single view of the customer across multiple data channels, they frequently have data that is inaccurate, out-of-date or duplicated. It’s difficult to use customer knowledge to generate more revenue when you have bad data that doesn’t reflect your business. It is also challenging to measure results or ensure that efforts to boost revenue are not, in fact, undermining it.

For example, a retailer developed an ongoing program to deliver discount coupons to frequent shoppers. Much later, after finally integrating, standardizing and analyzing customer data across the different systems, the retailer found that those frequent shoppers returned a substantial number of the products they bought. In fact, the program cost the retailer more money to process the returns and restock the inventory than they were making on repeat purchases. The data behind these conclusions came from a variety of systems, and without merging and rationalizing information from multiple systems, the retailer had no insight into the real revenue impact of this program.

Fixing Customer Data – Now and Forever

Companies worldwide struggle with customer data management challenges, and because no two organizations manage customer data the same way, there is no universal solution. There are, however, several data management principles that any organization can embrace to improve customer information.

Know what you’re trying to accomplish and what information can – and should – get you there. Companies collect massive amounts of information on customers, and all of that data feeds the overall view of customers. However, some of that data may be less valuable than other elements, and when developing a data management strategy, it’s important to focus on the high-value elements first.

For example, read any website about marketing and sales, and you’re bound to see articles about social media. Sites like Twitter and Facebook have enabled a new level of interaction between suppliers and consumers, and the landscape has changed.

If you’re a B2B organization, is the social media revolution as compelling as it would be for a B2C retailer? Probably not. Gathering and managing data on social media might be imperative for one company, but for another it may be a “nice-to-have.” The same goes for any other type of customer data – find the data that helps you reach your goals and start there.

Understand that the solution for corporate data is not just a technology problem. In fact, inconsistent, inaccurate or unreliable data is a symptom of poor data management practices. For example, one retailer started to load customer data into a new customer warehouse and found that email addresses in the existing records were mostly just a single character. The company learned that its call center staff did not have enough time to enter an email and circumvented the process with a single character to get to the next screen.

Poor-quality data is typically a symptom of larger problems. In this example, the bad data helped pinpoint a bottleneck in a customer-facing process. Once the process improved, better data began to enter the call center system – and the retailer had a more effective customer-facing process.

Agree to – and enforce – a set of standards. This could start with something as basic as answering “How do we define a customer?” Often, departments may have different views of what constitutes a customer. Sales would view a customer as anybody who has bought a product, but billing may consider customers to be anyone who pays an invoice. For business clients, this can become especially tricky, as there may be a multitiered network of contacts within a customer account.

The answer for defining “customer” starts with an internal discussion about the definition, as well as what information is required for customers and how systems will manage that data throughout the organization. Again, this isn’t a technology decision – it involves understanding the ground rules for customer data throughout the organization.

The key is to put that into practice. Data management technology is well-suited to analyze the data you have, implement rules to address poor quality data, integrate data across systems and enrich records with additional data elements. Your corporate data standard can then be implemented within the existing IT infrastructure. This means that data entered by marketing staff will undergo the same type of scrutiny as data coming from the customer support group. This is an important step in delivering a “single version of the truth” about your customers.

Customer data is obviously a key factor in acquiring and maintaining happy customers. Because every decision is based on the information in your systems, the data that supports customer-facing roles like marketing, sales and customer support drive every interaction. And they can be the difference between a profitable customer and an ex-customer.

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