Now you can predict the future – using something more scientific than a fortune cookie. Predictive analytics and big data have been blended to produce Predictive Marketing. It gives you the ability to identify which firms will buy your product in the near future.
The technique is remarkably simple, we just didn’t have the right tools before. There are a number of companies entering this new industry, at their core they are mostly based on the same method – analysis of existing customers followed by data extraction from web pages.
Using this method you can build a list of firms that are displaying similar buying patterns to your existing customers. It’s all based on behaviour.
Most organisations already use the services of a Data Aggregator, but I believe that as an organisation’s marketing becomes more sophisticated, they will progress from using Data Aggregators to Data Predictors.
- Data Aggregators – collect and append general business data – contact info, demographics etc.
- Data Enrichers – collect and enrich activity data with insights relevant to marketing
- Data Predictors – apply mathematical algorithms to the data and produce optimised prospect lists
Predictive analytics, predictive lead scoring and data science
Predictive analytics makes forecasts about unknown future events. It uses many techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to analyse current customer data to make predictions about the behaviour of prospects.
Predictive lead scoring, which ranks sales leads in order of propensity or likelihood to buy, is only one aspect of predictive marketing – others include predictive segmentation, predictive lead generation, predictive forecasting, predictive messaging – basically applying prediction to every part of the marketing process.
Data science was once the monopoly of huge companies who could afford teams of expensive data scientists, but this knowledge is now available to almost everyone through affordable predictive marketing SaaS systems.
Predictive marketing combines all the elements of analysis, scoring and data science to each stage of the marketing process; throughout the buyer’s journey and across every channel of communication.
Still not clear? Here’s an example.
Let’s say you have developed a software product for recruitment managers, enabling them to effectively post several job adverts on multiple websites with a management dashboard. Great.
After establishing the DNA of your existing customers, Predictive Marketing will search the web for similar organisations. For example, it might extract data from the careers page of company websites and calculate if there has been an increase in the number of jobs posted.
It might also do the same on recruitment websites to ascertain the likely recruitment budget of each company and confirm they are increasing the size of their workforce.
In addition, it could extract data from other publishers and blogs to find the reason for the increase in recruitment activity (new factory, major new client, new products, etc.) and also attempt to put a figure on the number of new staff required.
This ‘digital footprint’ is processed using real-time, big data and smart algorithms to rank which firms most closely resemble the behaviour of your existing customers and provide the best opportunity. The result is an ideal prospect list with a score next to each organisation.
Naturally, the higher the score, the more likely they are ready to buy. The marketing or sales department then contacts or nurtures the prospect accordingly.
This industry is in its infancy, and I would estimate the data supplied by some is only 60% accurate. But they are getting better each month by fine-tuning their algorithms and using test & learn strategies. At best, predictive marketing will be 80% accurate… and for most marketing managers that is accurate enough.
