A tracker is a strategy for collecting valuable input and data over time for your business. By tracking the evolution of KPI’s and open-ended feedback on your business, you gain valuable insight into how your customers feel about your product or service, what you could be doing to make improvements, and more.
That being said, evaluating tracker feedback can be time-consuming and tedious – especially when it comes to assessing open-ended feedback. In this article, we’ll take a look at why you should use a tracker and how to automate trackers for optimal efficiency and performance.
Why Use a Tracker?
An ad-hoc approach to gathering intel about your business generally means that you wait until necessary to deploy a survey. For example, you may see that your churn rate is quickly increasing, so you quickly design and implement a CX-focused survey. It’s a reactive rather than a proactive approach to ensuring high satisfaction and retention.
Trackers, on the other hand, are used regularly to collect data about a product or service. The advantage of this kind of approach is that you act both proactively and reactively – taking consistent steps to ensure high retention & loyalty as well as responding to key feedback and insights to make improvements.
Trackers are most effective when following pre-defined KPIs (Key Performance Indicators), upon which the business relies to achieve its targets. Popular examples are:
- CSAT (customer satisfaction) – Tracks overall customer satisfaction.
- NPS (net promoter score) – Tracks customer loyalty and the likelihood of customer referrals.
- eNPS (employee NPS) – Tracks employee loyalty and referral likelihood.
- Retention – Measures how well you are retaining customers.
The intelligence to populate these KPIs can stem from various data sources. Some examples are:
- All kind of surveys (qualitative and quantitative)
- Chatbot discussions, performed on your website
- Social media data, such as how many follows/likes/comments you’re getting
- Product mentions, brand comments and opinions from online reviews
Trackers have been found extremely valuable when collecting data from unbiased survey formats such as open-ended questions. These formats produce authentic responses from customers who are interested in sharing their thoughts, opinions, and actionable insights. By assessing this feedback over time, you’re able to identify and detect issues related to customer experience and satisfaction and put those issues into perspective.
For example, are these ongoing issues, or one-time incidents? Identifying trends in your open-ended feedback will give you insight into what kind of strategies will be most effective and productive in terms of boosting customer satisfaction and loyalty.
What’s the catch?
Trackers seem to be good. Trackers based on open-ended answers seem even better. Why is not everyone using them? The reason some businesses have decided not to pursue this kind of market research has often been: historic price & time. Analyzing these large amounts of data on a regular basis, especially if the data stems from open-ends, used to be expensive and used to take too much time to generate actionable insights.
The good news: latest developments in artificial intelligence (AI) have been able to lower these expenses substantially. There are a lot of businesses that have now realized the potential to install trackers and to actively rely on them for market intelligence.
Coop, the largest Switzerland-based retailer with over 2.5 million members analyzes sentiments over time to generate insights around specific topics and changes. When Coop implemented a design change to its store, the retailer was able to collect data to measure the impact on customer satisfaction. The result was a success: Coop noted an increase in satisfaction with the store atmosphere from 5% to 9%. Gathering this intel helped Coop gain insight on how successful their efforts were, and how to move forward in the future.
Automating Trackers: The Benefits
AI algorithms can automate trackers to help give you fast, accurate insight without needing to perform the full analysis yourself. AI can “learn” how to best assess your feedback, adapting with increasing accuracy with each new wave of feedback. In fact, AI will deliver you better quality analysis than you could have produced yourself. Automated analysis is far more consistent than human analysis because it adheres to an inexhaustible coding algorithm. Ultimately, automating large-scale trackers may even produce brand-new insights that could have been missed by human eyes.Automated analysis is far more consistent than human analysis because it adheres to an inexhaustible coding algorithm. Click To Tweet
At the end of the day, automating trackers is a far more efficient, convenient system for gaining insight than sifting through feedback yourself to identify trends and categorize responses. You save significantly on time and money – gaining more resources to focus on making improvements and developing actionable, responsive strategies to feedback.
How to Automate Trackers
By now, you know that automating trackers is a valuable strategy for creating a more efficient, accurate system for gleaning insight for your business. Automating trackers can help you to react quickly and responsively to new trends and customer feedback. That all sounds great, you might say. But how do you do it? 🤔
Generate a Codebook
To effectively automate trackers, you’ll need to create a codebook – a collection of topics, where topics might have one or more sub-topics, or codes. These codes are used to interpret the feedback, helping you to quickly and accurately identify common themes on a conceptual level. Your codebook will evolve as artificial intelligence learns how to make more accurate assessments of your trackers.
There are several different strategies for creating an initial codebook:
- Use a template as a starting point. This can either be industry-specific (such as a template used for the SaaS industry) or topic-specific (such as a template used for NPS surveys).
- If available, use previous codebooks that have been created for your product or service.
- Start from scratch. You can learn how to build a meaningful codebook here.
Refine the Codebook
The key to refining your codebook – allowing you to get better results – is time.
After the first wave of feedback, your codebook becomes more and more accurate. With each new influx of feedback, allowing artificial intelligence to apply the same analysis. This generates consistent assessment and gives you accurate insight into topics over time. Artificial intelligence will also intelligently respond to new topics that pop up with each new wave of feedback, working to give you insight into new trends, opinions, or issues.
Create Visualizations That Help You Understand Your Data
Data visualizations, such as the one below, can help you better understand your data. Creating a chart that shows the growth of specific topics over time is a valuable strategy for responding to big-picture growth–and seeing how changes have impacted customer satisfaction, customer loyalty, retention, and more.
Lean Back & Enjoy Your Efforts
Once you’ve created a system for automating trackers, you’re ready to lean back and enjoy ongoing, accurate analysis. Whether you use a tracker to evaluate Net Promoter Score, CSAT, retention, social media data, or another metric or source of information, an automated tracker will give you the insight to improve your product or service, boost customer satisfaction, and build a stronger business.
Caplena uses a combination of artificial intelligence and human intelligence to help you automate trackers, deploy powerful surveys, and accurately assess open-ended feedback with NLP (natural language processing). It’s a powerful way to deliver you valuable insight–and save you time, money, and effort. Click here to check it out.