These days every customer engagement action is a result of data-driven customer insights. Many wonder what happens behind the screens where the end-user interacts with the product/app. If you are one of those “many” wondering how data-driven customer engagement works, this blog is for you.
Businesses need to know their customers ‘ interests to run a successful digital campaign or plan future marketing efforts. Hence, user behaviors were tracked and also forecasted to meet business goals.
Starting with business goals:
Everyone knows customer interactions need to be tracked to deliver insights concerning customer behaviors.
But where to start?
What to track?
How to evaluate success?
These are fundamental questions, yet tricky, and they lay solid foundations for the rest of the insight-driven process. It starts with “business goals” and what businesses want to achieve by tracking user/customer interactions.
Once the businesses define their goals descriptively, it’s time to convert them into quantifiable and measurable SMART goals. This sets the direction for any future customer engagement campaigns. Now comes the question of “Time frame” – How long will it take to achieve this particular goal. Time adds focus to a specific goal.
The “Goal setting” process lays a strong foundation for the entire customer engagement process, and the context of engagement is set by the goals and the defined time to execute the same.
Track the needful:
When it comes to tracking data, businesses tend to track everything related to their end customer. But it’s a vague objective that ends in collecting large amounts of data with no purpose and direction. Following a goal-based approach comes in handy in such situations to figure out how a goal can be tracked with available interactions and metrics.
For obvious reasons, companies rely on Analytics tools for customer insights. They are easy to use and scalable. Hence, every business owner opts for one or more analytics tools to figure out the best possible customer insights out of the user data.
Analytics, when viewed as reports, doesn’t add much value to a business. It is the ability to make insights out of the data that truly makes a fundamental difference. However, few reports are self-explanatory and insightful at the same time—for example, Funnels. Funnels help businesses figure out where their users are heading or dropping off. A journey report specifies how users are navigating throughout the product with conditional statements in place.
Before and After story:
Every campaign goes live to create a meaningful impact on specific numbers in the KPI dashboards. It is equally essential to curate a story for each campaign, considering the user behavior before and after implementation.
Retain before lost:
Retention reports and last-seen reports are critical when calculating expected churn or conversion for a futuristic period. Thus figuring out the users who didn’t use the product in the last 30 days and re-engaging them to make them active and running exclusive campaigns for “about to churn” users can boost the customers’ conversion engagement rates.
To run a digital campaign is always a tedious process. But when a campaign is insight-driven, targeting few metrics as per the defined goals, the engagement process becomes more organized. Over some time, all the campaigns being executed as planned might have made a success story for the company by raising the bar’s metrics. Success stories and case studies speak for themselves in any kind of organization.
Segment and analyze:
When analyzing data or reports, demystifying the whole user data into specific segments and studying them significantly change. Every segment is unique in its way, and figuring out what works and what doesn’t for a particular segment will benefit businesses.
Track the voice:
The results of a campaign, especially for surveys, would reveal the pulse of the users. Sentiment analysis and text analysis can help businesses figure out the users’ overall opinions without independently looking at each user comment. Thus the technology can fasten up the process and would be more precise in producing results as needed.
Every account manager /analyst has a way of analyzing data. However, jumping on to the problem statement without a vision for problem-solving can do more harm than good. Thus, it is recommended to follow any kind of structure thinking methodology with different frameworks depending on the problem to be solved sets the foundation for the whole process. Later on, considering the prime reports for problem analysis and correlating data for every hypothesis built by the analyst comes in handy. But, there is a need for extreme caution from data collection to the conclusion stage – which can turn the tables if not done the right way.
There are many ways to incorporate the data analysis into a data-driven organization, but opting for omnichannel tools that are easy to plug and play is good to start with and helps scale as per the need, cutting out all the unnecessary costs. Upshot.ai is also one such award-winning omnichannel user engagement tool that can boost user engagement, conversion with the help of data and report analysis. Do schedule a demo to know more about it.
About Upshot.ai :
Upshot.ai is an omnichannel, user engagement, and gamification platform that helps digital product owners and marketers improve their product adoption and conversions. Fortune 1000 companies such as GE, UHG, Puma, Sony, ITC, Tenet healthcare are using Upshot.ai and observed a massive increase in product adoption YoY increment in revenues.
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