No sales process should ever be passive. Sales teams may effectively create methods to move a lead farther down the sales funnel, even if ultimately it comes down to the customer's decision to make a deal. Utilising data and predictive analysis can help to guarantee that your approach is successful.
According to Digiday, between 2017 and 2019, The Post and Courier in Charleston, South Carolina, had a 250 percent increase in digital subscriptions (from 1,700 to 6,000). Following a shift in emphasis from counting pageviews to tracking time spent and engaged minutes, it did so.
In the publishing sector, the value of accurate engagement metrics is rising. The Financial Times' North Star statistic is an engagement score based on a combination of recency, frequency, and volume.
What is Engagement Scoring ?
The method of rating users according to their likelihood of becoming paying subscribers is known as engagement score. To effectively devote key resources (time, attention, and money) to the prospects that are most likely to convert to a paid membership, marketing and sales teams can use engagement scores.
Systems for measuring engagement give value to a user’s activities in the sales funnel. Publishers will be better able to close more deals and convert more users to subscribers if they can determine which users are hot prospects.
Machine learning (ML), which uses predictive modelling algorithms to assess users data from prior interactions and project future customer journeys and results, is one of the most efficient methods for engagement scoring. Continue reading to find out more about its advantages and how to use them to your organisation's advantage.
Let’s see examples of metrics used for engagement scoring:
- Page visits
- Pages per session
- The time spent on the website
- Bounce rate
- The number of likes, comments, and shares on social media
- The number of return users
- Tracking regularity (daily and monthly active users)
- App downloads in case you have a mobile app and many more
Because it increases total conversion and aids teams in coordinating acquisition and subscription goals, a predictive engagement score is currently preferred in the marketing automation sector. The benefits of engagement scoring are:
A Quick and Complete Approach
Compared to manually collecting and analysing consumer data, predictive engagement scoring is faster. Publishers get immediate access to their data so they can prepare how to approach a prospective user. Additionally, it provides sales representatives with in-depth knowledge of the user's journey and how they interact with the content. They may then tailor their approach and offerings depending on what they believe would best engage the users.
Less prone to mistakes
Predictive engagement scoring, as previously indicated, is based on information obtained from demographics, transaction history, and consumer behaviour through the use of the company's customer relationship system. This data-driven approach improves lead scoring accuracy, with little to no mistakes.
Based on the typical behaviours, demographics, and interests of its target consumers, predictive engagement scoring enables a publishers’s marketing team to design and implement more targeted advertising campaigns and promotions. You may get the most out of your marketing and advertising spending by running more focused campaigns.
Using customer engagement scores for segmentation
By their score, you may also divide up your paying users and trial participants. For instance, you may identify user groups with scores between 80 and 100 who typically renew at a very high rate, 40 to 80 who renew at a 50% rate, and 1 to 40 who renew at a very low rate.
You may then request the following from your customer success or account management team using these segments:
- Spend less time on the 80-100 group because it is more likely that they will renew their subscription.
- Three months before the renewal, make a call to the 40-80 bucket and pay them a bit more attention.
- Utilise email marketing journeys, targeted digital advertisements, and in-context communications to nurture people in the 1–40 range until they are in the 40–80 area.
Tips to Increase Your Customer Engagement Score
Try gamification with your app
You can try to employ an engagement strategy that includes in-app gamification once you've recognised your user activity and located the ones with lower ratings.
You may use gamification to incorporate interactive elements into the content experience process, such as leaderboards, badges and discount coupons.
This strategy will only generate recurring interactions, raising your content engagement score. It has a strong relationship with the human instinct to respond to competition and act if there is a reward involved.
Your low-scoring users will require a more focused strategy as well as more care and time.
You may improve your engagement score and create a unique user experience by using a customised onboarding process, adding certain critical activities, and generating checklists.
To learn more about this, read our blog Online Personalisation and Engagement.
Pay attention to users who have a high engagement score
You might be surprised to learn that engagement scores are not only insightful when they are low, but also extremely valuable when they are high.
What can you accomplish with a high engagement score?
Well, it can be seen as a wonderful chance for development.
With the help of their scores, they identify clients who are optimally engaged and are consistently gaining value from your content. Then, sketch out their path to get a comprehensive understanding of the procedure.
What exactly do they do well? What aspects of your content appeal to them? What are the primary distinctions between the users who received high and poor scores?
Communicate with users when something’s wrong
At this stage, it's important to comprehend what's causing friction and resulting in disgruntled users. You must closely monitor users actions and score reviews in order to be able to intervene if something goes wrong.
You can increase customer retention and extend the average customer lifetime by looking at engagement indicators and helping consumers as soon as their scores begin to decline.
To learn more about Machine learning engagement scoring contact us today.