Data-Driven Storytelling Can Lead to Success
Analysts are aware of the storytelling data can share about the data integrity. There are also stories that lurk inside the data that can be used by copywriters and design teams. Let’s explore how these two kinds of stories can lead to success.
Data That Sticks Out
Analysts learn early in their careers how to scan a set of numbers for obvious discrepancies. It does not even matter what those numbers represent to see an integrity issue that needs more attention. In the chart below, there is clearly something amiss for response rates to change so much from one season to the next.
The storytelling here lives inside the data-driven world where analysts strive for data integrity and is driven by checking data for errors. In the case below where response rates vary so much, the analyst must work to fix the data error or explain with a story why performance changed so much.
Informing Copywriters and Design Teams
The second type of storytelling that leads to success is when data is leveraged to inform copywriters and design teams to drive sales. Here’s the challenge: how to link disparate pieces of data to a single customer record in a way that reveals an actionable path to purchase. Let’s break down that challenge.
Data-driven analysis is exactly the tool necessary to inform and enable our marketing teams to target content and specific messaging. Sending the same content to all customers is no longer relevant to all of them. We have to segment and target groups of customers to compete successfully in today’s world.
Customer journeys and life cycle intelligence are the basis for targeting in today’s multimedia environment. Consumers are using many different platforms AND devices in their shopping experience. The data from all of these disparate sources has to be combined, attached to a single customer record and mapped to a specific behavioral path. This visibility becomes the foundation for success.
The main obstacle to tying various tactical marketing data together is access to a data warehousing platform where data from many disparate marketing tactics can be stored, deduped and segmented.
The cost of this type of platform has changed significantly in recent years. It is far less expensive than it used to be. Make sure your assumptions are not 20 or even 10 years old! Having a single data warehouse is extremely doable now, and you can be sure your competition is doing this now.
The only way to get a clear view of a customer base is to have a lot of behavioral data tied to a single record identifier. Anything less will be incomplete and increase the risk of misdirecting the storytellers. Marketers are targeting segments by the books they read, the movies they watch, the products they buy and their Web browsing patterns. All of these data points, plus many more, are being collected daily and need to be tied to a single customer record.
Analysts already have a lot of data from testing:
- covers to get customers to open direct mail,
- email initiatives to increase open rates,
- headlines to increase paid search clicks and
- landing pages to increase conversions.
In addition to the most common customer journey elements previously listed, there are countless more. Analysts are getting really good at making sense of data, loads of it, to target customers with precision.
What matters in order to inform the creative storytellers is to map all these data points into customer journey and life cycle segments. A journey is traveling from one place to another. For data analysts, this means identifying a path to purchase that is repeated by a scalable number of customer records. In the graphic above, let’s look at a simple example for mapping a customer path that is available to all, the path a customer takes after landing on a web page.
An analyst can identify a scalable group of records that start on the same landing page, click through a common path over three transactions and eventually either convert or abandon. It is at the time of abandonment that the opportunity exists to get a relevant story in front of those customers.
Let’s add some more data elements that can also be attached to a customer record that abandons the cart. The device that people tend to purchase on, along with the time of abandonment, are available in most web analytics programs. Male/Female as well as the presence of children is widely available to all marketers. In this example, we now have many disparate data elements tied to a customer record. These are specific products that were abandoned in a shopping cart, a preference for using a tablet for shopping along with time of day and the presence of small children in the household.
This is exactly the type of data that can inform a design team about a scalable number of records that they can tell a targeted story to. In this case, a photograph is shot with a female using a tablet, small children styled into the photo and a headline that includes the product benefit of the web page they just abandoned. This targeted message is then delivered at the time people are shopping, in the format most readable on their preferred device.
Once data-driven purchase paths are ready to hand off to the marketing storytellers, the role of data becomes one of tracking the results. What creates sales growth is crafting a message that the customer can feel. Wow, that is a very powerful statement and where data telling stories come in. What is important is to make sure the storytellers have as much customer journey information as possible to mix into their bowl of magic. The more informed a purchasing path a storyteller can target, the better they will be at serving up sales.