By Anders Ekman, DataMentors
Consumers expect to be able to easily interact with their favorite brands according to their preferences. Whether they are in your store, shopping online or calling customer service, your customers want personalized and consistent experiences through each of these channels. Positive customer experiences create happy customers and when customers are happy, huge opportunities open to increase revenues and grow your business. In fact, research shows that multi-channel consumers spend 82% more per transaction than a customer who only shops in store.
There’s only one problem. Getting to know your customers this well is hard.
For many retailers, pulling disparate data sources together from every point of contact is a very big challenge, often overwhelming, that they face every day. Many aren’t even sure where to begin. Without this view, retailers are hard-pressed to even understand omnichannel behaviors. This is the first, critical step to unlock the value of omnichannel marketing.
The First Step to Omnichannel
Consumers interact with retail organizations through a multitude of channels, such as email, customer service departments, call centers, social media, in-store visits, and online shopping. Each and every touch-point is an opportunity to enhance customer value and increase profitability. To do so, data from each of these channels must be collected, linked to customers and integrated into a central marketing data mart or warehouse for analysis and marketing.
Just how much of a challenge is it? Consider this:
The number one reason for CRM failure is bad data. On average, every 30 minutes, 120 business addresses change, 75 phone numbers change, 20 CEOs leave their jobs, and 30 new businesses are formed. (Dun & Bradstreet)
Data often resides in separate systems and in various formats. For example, customer service calls may be kept in a separate system from customer purchases. Each of these interactions contains important customer details, that when combined, begin to create a complete view of the customer. Before creating a customer data warehouse, data must be standardized and cleansed.
A good first step is to evaluate the quality of your data with a data assessment that will help identify areas where data quality can be improved, what types of customer information may be missing, and other data problems that must be corrected.
Data quality software and processes should be put in place to integrate multiple data sources and automate data quality processes. They will also cleanse and standardize data so consistent formats can be integrated. You should also gain the ability to integrate any number of data sources residing in varying formats and multiple silos to create a comprehensive, 360° customer view. Include data such as e-commerce transactions, point-of-sale purchases, digital behavior, customer service calls, and credit card transactions.
The software needs to consolidate customer data into a single record, eliminating duplicate data, then monitor data to ensure it remains consistent and continues to align with business rules. It will also append missing customer information for a more complete view of the customer.
It’s worth every step. One study showed that inaccurate data has a direct impact on the bottom line of 88% of companies, with the average company losing 12% of its revenue.
With a clean, highly granular customer view, retailers can identify new opportunities to better target prospects and maximize customer value.
And the payoff is huge.
Here’s a good example:
A multi-location furniture chain, was struggling with establishing a clean, single view of the customer. Without comprehensive customer intelligence, they could not adequately target consumers for marketing offers, resulting in wasted marketing expenditures and lost revenue opportunities.
Using a data management solution, they integrated seven disparate source systems, including POS, credit data and online behavior, and delivered a fully functional marketing solution in eight weeks. All marketing channels are supported, with emphasis on digital and mail.
The retailer can now perform a variety of customer-centric analytics, which combine online behavior and in-store purchase history trending over 7+ years. They can determine a demographic profile of their best customer over the last seven years and match in real time against a consumer file of new prospects within thirty miles of a store location for an outreach campaign.
This furniture chain can also identify a customer subset that has purchased from certain categories (living room, home office) and append with updated address and demographic data to execute campaigns for different promotions.
Now, they can accurately use profiling and behavioral trigger-based marketing automation to help them in their hunt for new opportunities, including:
• Use purchase history to determine when it’s time to buy something new.
• Cross merchandising (When you buy a bed, get $100 off a new mattress).
• Frequency campaigns (We haven’t seen you in a while).
• Special events and direct mail campaigns (private shopping events and special store events based on a customer’s location and total sales amount).
None of this greatly enhanced customer knowledge and the results it drives would have been possible without an omnichannel view. That’s how critical it is for retailers to be able to pull disparate data sources together from every point of customer contact.
For some retailers, the goal may be to identify new marketing opportunities with customer segmentation and analytics. Others may look to enhance customer experiences by better understanding customers, their channel behaviors, and what they plan to purchase next. Or maybe they are aiming for both. Regardless, today’s retailers all share the common goal of maximizing customer value through multichannel marketing strategies. And the only way to make that genuinely happen is to combine all that customer data into a single, omnichannel view that lets you reach your customer with message they want, the way they want it, wherever they are.
Anders Ekman is president of DataMentors, a full-service data quality, data management and business intelligence provider that leverages proprietary data discovery, reporting and analysis, campaign management, data mining and modeling practices to identify insightful customer sales and marketing directions.