How One Company Used Data to Rethink the Customer JourneyAugust 25, 2016
Just how personal do customers want their experience to be with a company when making a purchase? A few years ago, customers might have said that a company’s attempts to offer a unique and personalized experience felt too much like stalking. Now, with so much time spent online, those expectations have changed.
Customers know that the companies they purchase from have access to their interests and behaviors. As the consumers, they must be willing to share their information if they expect a seamless, intelligent, and relevant experience across every channel and interaction. In return, they should expect personalized offers, advance notice, targeted suggestions, and a high level of customer service. Customers must be fully aware that they are generating a rich digital footprint with every transaction, click, and movement that generates data. When that data is appropriately used, it will help cement a loyal customer relationship.
I make my living on the other side of this equation. We have to make choices about how we most effectively use that data. To realize the full value and potential of customer data, we needed to shift from that channel-, product- or message-focused approach to a behavior- and preference-based customer approach.
Customers demand personal and relevant offers that come at the right time for them, not the company. The challenge is to better understand your customer data so that you can hone the timing and relevance of your message. A rudimentary approach is to segment customers into fairly large groups based on some demographic data (age, address, gender, etc.) coupled with recent purchase history. Then present offers that seem relevant to those segments. This can be successful — but not always. An evolving approach is to develop customer data hubs with advanced analytics that enable one-to-one segmentation and real-time decision making. When coupled with a better understanding of where customers are in the buying cycle, these analytics allow us to take a smart, informed next best action that, most importantly, provides a relevant and satisfying customer experience.
Here’s how we managed that transition at SAS. Our evolution began in earnest six years ago, as we moved from email blasts to more personalized messages. Our goal was to uncover the right mix of messages and channels to better align and create increasingly refined customer segments.
Our team gathered data on customers’ buying journeys – whether they resulted in a sale or not. That’s harder than it sounds. We had lots and lots of data at varying levels of complexity and in multiple places in multiple formats. Plus, our business provides analytics, business intelligence, and software services, so our customers are often coming to us with complex problems.
First, we had to clean the data and get it into manageable and usable data stores. We used a three-step approach: First, data cleansing: correcting nonstandard customer data and removing duplicate records. Next, data profiling that enables better understanding of the data by uncovering related data across tables, databases and applications. Finally, entity resolution: identifying data from multiple sources and attaching them to a single customer.
For example, some data might be on a web channel, another set from an inside sales source. More records from the same customer might be found in contact center data. Being able to see how customer data moves through your organization is vital.
Once the data is wrangled and corralled, you can better manage it and set governance rules. Using analytics, we then compared the type of messages sent to a particular contact, that contact’s buying cycle phase, and the final outcome. We found that a lot of our messaging was misdirected and out of sync. For example, we were sending early customer journey messages to contacts after a deal was completed – whether we won or lost.
We also found that contacts were requesting content in one subject area (for example, analytics solutions), but those contacts were actually involved in a deal for a different solution (say customer intelligence), so they weren’t getting the right content.
Our analyses were extensive and resulted in some crucial changes in how we interacted with customers. Based on our customer data, we are better able to identify where the customer is at any point in their buying journey. For example, are they researching? Do they have an open sales opportunity and they are still deciding? Did they just buy something and are needing more information? Are they an existing user?
While customers’ content needs might be similar in some of the stages, the message and approach should be different. When someone is researching a purchase, we may not have enough data to fully understand their needs, so we’ll gather information and notify sales so it can follow up.
The easiest way to gather this data is by requiring some minimal registration information in the online experience. Our intent is to share our expertise and ensure they have all the information and resources they need. The follow-on messages they receive are triggered by ongoing interactions with us and the data we collect from those interactions.
We also use the data to better identify the most effective channels and content to engage customers to better fit the stages of our new customer journey life cycle:
- Need – High-level messaging, including thought leadership strategies (articles, blog posts, etc.). Content at this phase explains the problem and provides a path forward.
- Research – Content that validates the customer’s need to solve the problem. Material here focuses on specific business issues and includes third-party resources (analyst reviews, research reports, etc.).
- Decide – Deeper content that provides more product-specific information. This material validates the proposed solution through customer success stories, research reports, product fact sheets, etc.
- Adopt – On-boarding and self-service content. This stage focuses on introducing customers to support resources and online communities as well as “do-it-yourself” material that introduces the customer to the solution.
- Use – Adoption content, such as advanced educational information, user conferences and product-specific webinars. At this stage, users mature with their use of technology and turn to more technical resources to expand their knowledge.
- Recommend – Content specific to extending the relationship with the customer. This includes speaking opportunities, focus group participation and sales references as well as involvement in cross- and up-sell opportunities.
Based on the re-conceptualization of our customer journey, some of our key marketing strategies changed. For example, a major retailer came to SAS looking for information on customer intelligence software. What we failed to notice was that we had two different contacts from the same company looking at different, but related information. Because we hadn’t yet realigned our organization to have a more unified view of customers, we ended up sending them 30 emails during a 30-day period. Unfortunately, none of those messages had anything to do with customer intelligence solutions. Not surprisingly, none of those emails were opened or acted upon by the folks we sent them to. They likely ended up in a spam folder.
As we engage with customers today, we make sure our interactions are more meaningful. We are able to identify all of the contacts for a customer (and there may be many), the products or solutions they need, where in the customer journey they fit, and we provide messaging and information that aligns with their needs. After the sale, we engage them with relevant activities – invitations to join our user communities and support functions or provide other technical resources.
But all of this is only possible with the use of customer data. In the same spirit that we as consumers have become willing to trade information for personalization, we ask our customers and prospects to give us data about themselves (what they want to tell us), and how they prefer we interact with them. Our promise is to use it effectively when interacting with them.