In our last post, we discussed the building blocks of data, where it comes from, and the basic terminology needed to understand how data will evolve over the coming years. One challenge moving is forward is re-imagining data as a tool across an organization. Let’s start with how many companies currently use their base data tools for a marketing campaign.
The largest CRM platform in the world is Salesforce. It is one many of us are familiar with and many organizations use but the below applies to any of the popular CRMs on the market. Let’s look at the data flow from a lead prospecting or advertising campaign:
Seems fairly simple doesn’t it? The basic premise is that we use existing data to build out audiences you can target your advertising towards. As campaigns perform, use the data from both winning and losing campaigns to build better informed, future campaigns. It’s a very effective model and not one that we should move away from. Many companies refer to this approach as look-alike or act-alike targeting.
It’s an extremely smart approach but a bit narrow from a corporate perspective. In the above structure, your campaign analytics only stay within the marketing department. It is as if one of your legs knows that to get somewhere it has to move forward, while the other leg has not yet received that information. Ultimately, we either end up dragging the other leg along, but we can only go so far.. We need all of our limbs working in concert to get to wherever our destination may be.
So, let’s make the above flow a whole lot less narrow. Let’s use retail as a use case, because a very smart man once said, if it works in retail, it probably works everywhere.
As you can see, the map has expanded by quite a bit. No longer are we confining our data to one segment of our company but mapping out data across the entirety of our company. Let’s apply the above map to a hybrid (digital/brick and mortar) retailer, HomeGoods. A customer, let’s call her Becca, walks into the store (either online or in person) and buys a set of copper bottom pots. Now Becca is unaware of it, but she is very similar to a whole lot of other customers and has set in motion numerous events that all occur simultaneously.
The inventory team needs to replace the pots Becca purchased. These were very popular pots, so thankfully, our DMP has informed the inventory system to keep more of those pot sets in the warehouse. Phew.
The product or operations team notices that these particular pots are selling like hot-cakes. So they start to sell pot scrubbers because all pots need to be cleaned. Maybe they expand the number of copper bottom pots they carry and bring in some more other brands to some higher and lower end price points.
The marketing team sees that copper bottom pots are a hot selling item and begin pushing those pots to audiences that look and act a lot like Becca. They can test extension lines or the different price points and report their findings back to the product team.
Our retailer is now a whole lot more agile. Obviously, the data applications here are significantly broader. Maybe different products are carried online vs. brick and mortar depending on purchase patterns and advertising performance. An important point to note is that this data structure used systems that are already in place. All we did was create a central point that automatically gives relevant data to the relevant teams to create a significantly more efficient and better performing organization.
Now that we’ve seen the evolution of data for a retailer, we’ll begin to expand into other verticals and add more data points. Check back again this week for the next article in our data series!