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6.9.2018 - 9:36 ID BBN Guest writer

Existing data predicts customers’ future needs

There’s a lot of talk about machine learning and artificial intelligence. But most companies can’t see what purpose they serve.

Companies have a lot of customer-related data like billing information, sales figures, support requests, leads, marketing communications responses and so forth. The data has often been fragmented into different systems and functions. The finance department analyses business figures, sales follow the pipeline, customer service has its own metrics and marketing analyses the impact of marketing activities.

Only rarely do companies have a clear image of which data assets are significant for a successful customer relationship. New measurement methods and tools are created to try and predict customer loyalty, but at the same time we forget to utilise the existing data, because who would thinks to look back?

Data is guiding marketing

I still remember the time when it was really challenging to measure if a marketing campaign had been successful. A campaign was considered successful, for example, when people belonging to the target group remembered having seen the company’s ad in a magazine. Today it’s commonplace to measure marketing effectiveness and added value in euros. New technologies can help us collect large amounts of data, which helps us get information about precisely the things that interest us. We can analyse history, interpret the status quo or predict what’s coming.

Predicting churn and purchase interest

Every one of us has surely read about how digitalisation has altered B2B buying behaviour. Defining buyer personas and customer journeys is a familiar exercise for many companies. But how many have made use of their company’s own data for predicting churn or for recognizing readiness to buy among customers?

We can get a pretty good idea about trends based on the right data, but machine learning can bring up surprising facts. For example, how the number of support requests and actions in digital channels can affect loyalty. By analysing history data and utilising machine learning, you can create predictive analytics models that help recognise customers who are about to leave or potential customers who are about to make a buying decision.

Data will take the lead in work and decision making

Utilising data with the help of machine learning brings a whole new perspective to what we are doing in our company today. My own motto has for a long time been “numbers don’t lie”. I strongly believe that by utilising technology, analysing data and creating models for predictive analytics, our processes will become more effective and we can offer a more personalised service to our customers where it matters most. Collecting and analysing existing data can offer significant added value to your company’s future success. How will data affect your company’s future decisions and operations?

About the writer:
Milla Ikonen is Marketing Director at Visma Software Oy.

Come and listen to Ikonen talk about Visma Software’s journey towards predictive marketing at our next Power Lunch, Thursday 9th October at G Livelab in Helsinki!

Register here >

 

Tags:

Artificial Intelligence
Data-Driven Decision Making
Drivers to Success
Energized Programs
Marketing Technology
Predictive Marketing
Synchronized Martech
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The ID BBN guest writer is a selected expert in his/her own field. The guest writers address interesting and current phenomena.

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