Practical ways data analytics can help with existing product adoption
Have you ever fallen into the role of being an existing product owner and know little about who is using your product?
Do you know how many people use your product, how well it functions, if it achieves its goals and what are the drawbacks and obstacles which require further improvement?
Data analytics can be your friend here, helping in all sorts of ways to get these answers.
Definition: By data analytics I mean of course the slicing and dicing of data, to get objective information that can provide meaningful answers to questions you raise, and so result in you taking action to achieve some goal.
I’ve been put in this position before, where I was taking over a role from someone unexpectedly and didn’t know who used this application.
Talking to people gave me some idea of who should be using it in my organisation, I knew who had access to it, but I didn’t know any more than that.
How often it was used, whether it was a success and helped them, what problems they had with it, and whether they liked or disliked using it.
So how did I solve this? First, I made a list of the questions.
1. Understand what questions you want to be answered.
Make a list. Seriously do this before looking at any technical solution. Always.
Some questions I asked were:
Who is using my product?
Who is not using my product who should be?
Are people using a particular area of a product more than others?
Are people not using a particular area of a product, where they really should be?
What areas are slow, hard to complete, or not filling their purpose?
Note: It’s also good to note what you’d LIKE to see, in terms of adoption, use or performance.
2. Collect data.
We then implemented monitoring in the application, so we understand everything about what was happening. E.g., Which pages were visited, how long were people on a page, who clicked which button, etc.
Not only that, we created surveys to capture what people thought of the product as well as creating feedback channels so if they wanted an improvement they had a way to go about requesting it.
Here are some examples:
Google Analytics
If you’re using a website this is fairly easy to integrate into your product and provides heaps of data. Understanding it is the next trick.
See: New Google Analytics
Type Form
While objective measurements of usage data, process monitoring and other data capture are vital, it is also key to capture subjective information in a way that you can use.
Typeform allows this with easy to create surveys, see: Typeform
User Voice
The people who use your product are almost certainly experts in what they do. Making their life easier is the easiest way to keep them using your product, and to expand its future functionality. UserVoice allows all of this easily. See: UserVoice
Video Ask
Video is BIG. People expect better user experiences now, not just in products but in all areas of their interaction with technology and people.
One way to achieve this and get a better connection is to use video with questions to answer key questions.
VideoAsk is perfect for this. See: VideoAsk
3. Analyse the data, turning it into information.
Once you have data you need to analyse it to see if you can answer your questions.
Many products come pre-built with a dashboard for this analysis, but you can use Excel, PowerBI, Tableau or QlikView depending on your needs to get to grips with your data.
Some of these require professional data & integration development. Depending on your requirements you might need to create this technical architecture or if you can it’s good to go with a simpler approach to get immediate results.
Some examples of products with existing dashboards which allow you to analyse data captured are:
Microsoft 365
This provides usage reports in many areas, to users directly through user reports, to administrators via the admin screens, or PowerBI dashboards like this.
Yammer
More specifically, Yammer Analytics sheds light on how people are using Yammer.
You can see the group analytics here.
Salesforce
Salesforce gives deep insight into business processes and how people are using solutions deployed in Salesforce. See https://www.salesforce.com/products/crm-analytics/overview/
In my example, we identified that we needed to implement periodic training to highlight new functionality. It also came to light that the application had one or two screens that were too slow. These were added to the backlog of changes, allowing us to remedy the situation smoothly.
4. Slice and dice and investigate. Have fun. Play with the information.
This step is often missed but is really really useful. It’s good to try and step back and take a fresh look at what the informative is showing you. It may be the case that it is best for someone else to look at the information if you’re too close to the particular problem.
Some points which you may notice:
1. New hires are not using the product, causing it to stop being used over time.
2. Key members of staff are using a different product instead.
3. The product does not work as needed on mobile phones.
In my example, we identified that a different business unit had its own product which served the same function as mine… which resulted in some interesting discussions believe me!
5. Take action & repeat.
As with anything, data analytics is pointless if all you do is look at it.
You need to report on the results, highlight them to process owners and improve the product.
The alternative is to see a product’s use decrease to nothing. Do you want that?
All products go through a product lifecycle no matter how good they are. (Google take note. Ha.)
Do you know where your product is on the product lifecycle, and where you want it to be?
Are you investing further in it to expand it is functionality, and deliver benefits to more people or are you patching it, with minimal effort to keep it running until the next product replaces it?
Regardless of where you’re at, you need to routinely make time to review data and amend your actions to get ensure you meet your goals.
In my example, using the information we received, we were able to justify a further increase in our budget, allowing us to enhance my product and so deliver a better experience and more functionality to my clients. One nice benefit to improve product adoption.
Some Tips
1. Know your target market/audience.
If you’re deploying your product internally, make sure you understand the organisation structure, and who should & shouldn’t be using your application.
2. Ensure new hires or new people joining a department get appropriate training so your product doesn’t die a slow death through attrition.
3. Create feedback channels so people can actively give you feedback formally and informally.
4. Understand your target audience through active engagement and investigation into what they need concerning your product.
5. Create a champion network. Data Analytics is useful up to a point, but having people on the ground, talking to people who want to talk to you can make or break a product.
6. Create training in the format your users want to receive it.
Information portals, videos, PDFs, interactive workshops are sure to cover what they want.
Product adoption is not a once-off activity at the beginning of a project. It is an ongoing activity that should be part of your Business as Usual (BAU) process that needs time and attention to get the full benefits of an investment.
Data Analytics is one way to shed light on where you need to focus your attention to help achieve it.
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