Once we made the shift to building products with a focus on product analytics, event tracking, and testing features, we made generous leaps in creating product ROI. Our clients’ apps were selling more, app ratings were going higher, and the number of customer support calls started decreasing.
According to McKinsey, “Companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.”
As a team, we all came to the same conclusion after seeing the results of implementing analytics at the beginning of projects: building products without an analytics and optimization strategy is a waste of time.
Educated guesses are still guesses. We will never build a product again without creating an analytics and optimization strategy at the very beginning of a project.
I thought I would share how we use analytics and optimization for our clients. If you care about revenue, profit, and product/market fit, then please read on.
Create An Analytics Strategy Before A Single Piece Of Code Is Written
Every product we build starts with an analytics strategy before a single piece of code is written. We know the metrics we’re looking to improve, and how we’re going to tag our events to measure these metrics. Through event-based tagging in tools like Firebase and Mixpanel, we’re collecting data on every important event in our clients' applications.
Here’s what it was like before and after we made the switch.
Before: Launch product and collect general analytics (but have no way to determine ways to A/B test or optimize the customer experience). Make educated guesses on what users of the application would prefer and build that into the sprint plan. Once live, we would make educated guesses on what features should be built or improved.
After: Clear identification of how users are using the app, opportunity areas, and clarity on where our clients should invest in product or marketing dollars. This resulted in:
- Increased revenue generated from eCommerce applications
- Clarified understanding of how users are navigating the application
- Identified the most profitable users and what makes them profitable
- Improved product retention and reduced churn
- Determined best marketing channels and campaigns
Define And Design Experiences Based On Greatest ROI For Business
We’re lucky to have product experts on our team who have a natural instinct to create great products. We use this instinct plus data-driven experiments to confirm our hypothesis and drive ROI. When we start projects, we often start with a core group of metrics to understand the value of the application we’re creating. We want to know what determines success for each client.
Example: The goal is to increase the conversion rate of mobile app buyers.
Here are the questions we will ask as we prepare our testing and product strategy:
- How will a personalized home page impact sales?
- Should we show a variety of options or limit it to a few that we’re confident the customer will like?
- Will adding manual preferences improve personalization and increase sales?
- Will offering personalized add-ons at the check-out screen maximize average order value?
- Before the check-out screen?
- Is it a checkbox or a list of items for them to choose from?
- Do we bundle it as part of each individual item, or all the way at the end?
- Will how we present the price change behavior?
- Will discounts work?
- Do push notifications result in increased sales?
- Which copy on push notifications works the best?
- What date and times bring in the most sales?
- Do push notifications for abandoned carts increase the chances of a customer checking the products out?
- Does customer loyalty result in bigger basket prices?
- If they are a registered member, do they order more?
- Does making it easier to register and store their credit card numbers increase the chances of return purchases?
As you can tell, a simple metric can drive a lot of great questions to optimize a path to increased revenue and profitability. Every one of these questions can be tested and validated.
For enterprises, the metric might be around the speed of closing customer service issues. Then we implement a plan to test the following to improve speed:
- How can we predict what type of problem the customer has? If we do predict it, can we help our customers solve their problems quicker?
- Does showing common problems up-front help them solve common problems?
- How can we personalize what is presented to give them the answer quicker?
Products Must Work To Evolve Themselves
Facebook runs 10,000 versions of its own website every day.
At any given point in time, there isn't just one version of Facebook running, there are probably 10,000. Any engineer at the company can basically decide that they want to test something. There are some rules on sensitive things, but in general, an engineer can test something, and they can launch a version of Facebook not to the whole community, but maybe to 10,000 people or 50,000 people—whatever is necessary to get a good test of an experience. ~ Mark Zuckerberg
There is a reason many enterprises are moving from a waterfall process to agile. They know they can’t possibly have all the answers up-front. Products need to evolve and teams need to adjust their way of thinking.
We give our clients, product teams, engineers, and designers the ability to test hypotheses on features they want to implement. We’ll test everything from copy to screen flow, even button colors.
The product evolves when the teams accept that there is no right answer, and the data needs to do the talking. Do the right analytics tagging up-front and implement A/B testing as soon as possible to help you make the best decisions. Build an intelligent tracking plan on day one and, if you follow through, you’ll see iterative improvements which can only result in positive results.