Leveraging Customer DataJanuary 16, 2020
One of the most challenging parts of launching an application, or product, is not in the creation of it, but rather validating its purpose. You’ve built something you believe speaks to a particular demographic, but how do they actually feel about it?
Companies try to determine this answer by quickly delivering some sort of minimum viable product (MVP). This has the advantage of being cost-effective and is a great approach to getting something off the ground. That’s great and all, but what metrics will you use to determine if your product is viable? How will you know where the value lies in your product? Is it the main part you delivered or is it a small piece that’s resonating with your users, requiring you to “pivot” in a new direction?
There are a number of ways to answer these kinds of questions, some being more involved than others. What we want to discuss here is how you can get the insights you need in a way that 1) allows you to effectively take action and 2) allows you to focus on bettering your product experience rather than how you get insights from it. Because sometimes it’s not the feature or function that matters most, it’s what the user is trying to tell you about that feature or function.
Overview on Tracking User Journeys
Let’s take a step back and picture someone using your application. Maybe they got there through an ad you served them, where they clicked around until some page was enough to entice them enough to sign up. Once their account was created, they performed a few actions a signed in user can do, before leaving and never coming back.
You then have another user with a similar story, but this one kept using your services. These two interactions, while being nearly identical, paint very different pictures. If you could get these two users in a room, what would you ask?
This is where analytics comes, hence why there’s such a buzz in the air about “big data”. It acts like an imaginary room where you can asks questions to your users. But we can almost get something better than sitting these people down in a room–their undisputed interactions with your brand. Rather than relying on the people in this imaginary room to tell you what drove their initial engagement, which they probably won’t remember, we can see the exact pathways and engagements they had.
We can then take these pathways and engagements, known as user journeys, and make them actionable. In the above situation, wouldn’t it be great if we could find the difference in those journeys that made the one user stay and then market that difference in a direct advertisement to the one that disengaged? Well, with a solid user tracking policy, you can do exactly that.
The best place to start is by determining the biggest business objectives you’re looking to achieve. These will change as you company grows and the plan is to have this evolve over time. That being said, it’s better to focus on a smaller list at first, which will allow you to ease your way in. If you said you wanted to get into whisky and someone sent you 100 bottles, would you even know where to start without consulting some resource? I bet not (though who can say no to that much whisky).
In contrast, if that person gave you a handful of bottles to try with a purpose behind trying each one, wouldn’t that be a much better introduction? In fact, I bet you could then go from there and try the 100 bottles with a lot more understanding and intention. Same goes for building a user tracking policy.
Once you’ve put together your list of objectives, we then translate how those objectives could be obtained through your various channels. Then comes patience. It may take some time for you to get enough data to make good decisions. So we sit, wait, and monitor. Once we have enough, we can analyze the data, take action, make any changes, rinse and repeat.
Step 1: Implementation Plan
Implementing a solid user tracking policy will provide you with the actionable insights you desire, as it’s all about understanding the journeys and interactions your customers have with your brand. It also has a large side benefit of allowing you to take this found knowledge and develop targeted marketing campaigns, empowering you and your business to make data-driven decisions rather than emotional ones.
There are five key metrics companies try and gain insight from.
- Acquisition: get new user to the site
- Activation: engage those users with your main offerings
- Retention: keeping the users coming back
- Referral: getting those users to bring more users
- Revenue: converting those users into dollars
Once we’ve determined the critical insights you’re looking for, we can then move forward to setting a base “language” to speak when it comes to our tracking. This will become known as our “Data Governance Model”. The purpose of this model is to not only keep everyone on the same page, but will enforce a level of consistency in the tracking plans across your organization.
Finally, we wrap up this section by determining when we want to have this plan in place, how long we want to keep it in place, and what would constitute taking each piece of this plan to the next level.
The biggest long-term failure in data analytics is not being specific about what is tracked and why. This ties into operations and business goals into quantifiable data so that actions can be created. The lack of an implementation plan often leads to an abundance of data with no real metrics that matter.
Step 2: Tracking Plan
This is the point where you connect your business objectives to the Data Governance Model you put in place. You and your team sit down and state exactly what interactions you want to track within your web and mobile experiences that directly correspond to the insights you’re trying to obtain, limiting yourselves to the “language” set in your model. This creates a consistency in naming convention with variables being ‘identified’ and allows validation to occur on events that don’t meet that criteria.
Step 3: Setup Connections
Now that we know what we want to track and why/how we’re wanting to track it, we now need to set up any upstream (sources) and/or downstream (destinations) integrations that will help us achieve this goal.
No matter what sources and destinations you set up, you’ll always want to forward all of your information into one or more data warehouses. This will take the data you’re gathering from all of our sources and dump them into a SQL database for us to easily gain further insights and complete control over what you’ve gathered.
Step 4: Implement Tracking Plan
This step includes getting a developer to place the tracking scripts into the website or application. S/he will then also add in any/all events related to the tracking plan we put together in step 2. For example: the identification of specific fields in a form to pass through with other data gathered about the user journey, referral source, past and future actions, etc.
Step 5: Creating Segments & Profiles
This is where things get really interesting. We can now take all of the data we’ve gathered from our sources and connect the actions taken to specific users. And if we’ve done our due diligence in identifying who’s on the site, we can even see some personally identifiable information behind the actions being taken.
Taking this even one step further is creating funnels of users that we can send their data to other sources, such as Google Ads. So we can query across our data warehouses and calculate the average purchase amount for each of them. From there we can then determine who are the high value vs. low value spenders, and funnel them directly into specific Google and Facebook advertising campaigns. We could even take that funnel of high-value users a step further by finding the ones who’ve been abandoning their carts and send them a targeted discount code.
Step 6: Analyze, Take Action, & Optimize
The final step is to regularly review and optimize based on campaign performance and user response. This will take a full monitoring team to help provide reports, metrics and review.
How LLT Does Analytics
We do this by capturing specific actions taken within our web and mobile experiences, funneling them into a unified platform. Once in this platform, we can then send that data across 200+ integrations, from advertising brokers like Google Ads to CRMs like HubSpot and many more. By operating this way, we can more easily provide insights such as, “What products did people buy after being shown a specific Facebook ad?” or “How did our sales team interact with a customer after they submitted a contact form?”.