I was preparing to speak at a big data event a few weeks ago. The audience was not a specific marketing crowd and I was wondering how to position exactly what Relative Insight does.
Thankfully, one of the speakers ahead of me was discussing predictive analytics in a compelling fashion and using an example of sales performance to make his point. Using predictive big data analytics, you can make recommendations on who to call, at what time of day, in which geographical area to increase your chances of call success.
So how does Relative Insight fit with this predictive big data world? Easy; we help you to increase your chances of call success because we can tell you what to say.
Different customers all use different language styles, with most customer groups clustering around some key common language traits and topics of concern. These customer groups will engage more with people who reflect their language and concerns. The trick, of course, is finding, measuring and developing this language.
This is the capability that Relative.ai provides to its customers. The ability to grab language from the internet, measure it, compare it and apply the insights so they can better engage with their target customers.
So let me run through an example of how this worked recently. One of our clients used us to answer the key question: ‘How do different customer groups think about the concept of ‘summer’?”
Our client was a retailer focused on mothers, and was considering whether to target its summer ad campaigns to different customer groups. To understand what their customers were concerned about and the type of language they used, we went to two popular online forums for mothers. We analysed the language used when discussing the concept of summer and compared the two groups with each other.
Concepts and language used by Group A far more than Group B:
- Days out to museums and the countryside with kids
- Places to go on day trips
- Holiday recommendations
- Topics were described as ‘beautiful’
- The discussion was generally positive
- Discussion on education
Concepts and language used by Group B far more than Group A:
- Food and feeding the kids
- The kids getting ill
- Having to buy too much stuff
- The cost of the holidays
As you can see, these are two customer groups who are concerned about different things and use different language styles to discuss them.
Using this data, our client took two critical actions with the language of their outreach:
- They addressed the topics of concern for both customer groups with different outreach strategies
- They used different language to target each of the two customer groups, reflecting their style, to increase engagement and conversation.
The whole exercise by the client was a good example of how analysing and comparing the language of customer groups enables an organisation to understand how to target their brand effectively at different audiences.
Taking the time to understand customer language can reap big rewards. It allows a level of insight that can pay off in a big way when applied to your messaging, flowing right the way through different marketing channels.