Connecting these sources of data, though, isn’t always easy (or possible). A simple example to illustrate this point is looking at two data sources that play a huge role in the customer journey: email marketing and web traffic. Many retailers will have a CRM platform configured to handle deploying email marketing campaigns and measuring performance metrics like the open and click-through rates, and a platform for measuring their web analytics, things like daily visitors and transactions.
Often these CRM platforms have built in page builders that allow marketing teams to quickly build and publish landing pages for different marketing campaigns. These are great tools as long as you anticipate and account for relying on the CRM’s native analytics to collect data on the pages, taking it away from your overall web data. Without good, clean ways to join these two customer data sets, there’s a huge gap in your reporting ability leaving you unable to answer important questions like how much should you invest in email marketing.
Mobile ecommerce sales accounted for 34.5% of total ecommerce sales in 2017, according to Bigcommerce. Retailers can’t afford to not connect their customers’ desktop behavior with their mobile behavior. Without joining these two data sources, customers will have incongruous experiences that cause frustration and put up barriers to purchasing.
Customers might be doing their purchasing research on multiple devices, including their phones, tablets, and home and office computers. If you want to understand that journey, you need to be able to stitch user behavior across those devices to get a complete picture. For marketers with long sales cycles, picking out each touch point on the path to purchase becomes even more important.
One Snowplow user, a marketer for a flooring company, was struggling to attribute sales to his different marketing campaigns; because of a long gap in the sales cycle, he was only able to connect campaigns to purchasing a sample, which cost less than $10. With complete purchases valued over $10,000, determining which ads drove the most sales and which only lead to buying a sample was a high priority.
Using Snowplow to join his data together, the marketer was able to describe what a typical sales process looks like. Customers, he discovered, will typically follow the same progression:
This customer journey typically takes 53 days, and because it crossed so many channels the company struggled with attributing their web ad campaigns to the sales occurring nearly two months later. With all of their data joined, however, they could look at how their customers engaged with their advertising, website, and email marketing, and tie that behavior to future purchases. Having unified data helped the marketer piece together the company’s customer journey, giving them a clear picture of which campaigns drove the most sales, where their most valuable customers came from, and what point in the sales cycle saw the highest drop-off.
At a certain stage in a customer’s journey, that person becomes a “known” customer- they’ve given you their name or email address or created a user account, something to identify themselves to you. This might only happen after making a purchase and entering their name and delivery address as part of checking out, but it could occur much, much earlier.
Many marketing campaigns are designed to get pieces of this information much earlier, allowing retailers to build a relationship with their customers well before they make a purchase. The earlier in the process retailers can connect with their customers, through means like good experiences or high quality product recommendations, the greater the ability to guide those customers through the buying process towards making a purchase.
That information is only useful, though, if we can stitch it all together. A user might visit your website several times before they decide to sign up for your email newsletter. We want to be able to tie that identity to the data describing that person’s journey when they were unknown up until that point in time when they become a known user.
Each customer will follow their own path to purchase, researching a company, its products, and its competition. Some customers will do all of these things, some will do none. When you join your different customer data sources together, you get increased visibility on your customers’ journeys at an individual level and in aggregate. At this point, you can identify the key activities that are recurring in most customer journeys, like reading a blog post or clicking on an Instagram ad.
With a complete picture of your customer buying process, you can answer important questions about how people go about purchasing your products:
Cleverly analyzing your unified customer data might not answer all of your questions outright, but will give you enough material to start forming hypotheses that can be tested and measured. Doing so will help you identify how prominently each activity features in the journey of a particular customer or segment and pick out any activities that are strongly indicative of a purchase.
For most retailers, who offer a selection of products, not all purchases are equal. This seems obvious- more expensive purchases are worth more. But, as you join your customer data together to get a clearer picture of your customers’ buying behavior, you’ll realize that’s not necessarily true. Single, high-value purchases might look great, but you may discover that it’s the smaller repeat customers who drive your business.
If you sell athletic equipment, how do people buying bicycles behave different from people buying tennis rackets? Which type of purchase is more valuable to your business? Looking at how certain products sell compared to others can help you plan what kinds of customers and transactions you want to drive more of. Understanding how different products or services you sell can help you optimize your customer mix.
Comparing your different products to see what key activities take place in each buying process is a great way to find any gaps where a customer might be taking longer to purchase because they’re missing essential information, or to spot opportunities to reduce the sales cycle by testing out new interventions like sending limited-time offers product recommendations.
If you use data effectively, you’ll better understand your customers and be able to use that information to support and empower them throughout the buying process, making it rewarding for them and increasing the likelihood they’ll buy from you. A long sales cycle can feel like a burden: without proper data collection in place and the ability to put that data together in a meaningful way, marketing to customers can seem like aiming in the dark. But, with the right tools, your data can help take the mystery out of figuring out what your customers want so you can deliver brilliant shopping experiences.
We’ll be covering each of the elements of understanding your sales cycle in greater depth in upcoming posts. To get notified of our new content and learn other ways retailers can be transformative with data, sign up for our newsletter.