4 Steps to Success with Predictive Analytics
By examining your historical business data, you may be able to explain what happened in the past and why. But you can also use these historical data to predict the present and the future, and even tailor your business processes to counter the predicted negative spirals and automatically sort for you prospects based on the odds of success. The technology is ready, but how to successfully implement your real-time analytics?
Let’s take a look at an example. Imagine a visitor surfing on your website and you have information that is good enough to understand the profile of this visitor, you can then adapt the content of the website according to visitors with a similar profile have frequently read or downloaded in the past. You then undertake a real-time action based on information from the past.
In the example above, the behaviour of the past serves in turn as a basis for influencing the behaviour of the current visitor and converting it more easily into a customer. By analyzing the characteristics of the companies, people and transactions that have become clients, we can predict the chances of visitors becoming customers by using the characteristics of prospects.
Companies often face a customer attrition problem, which they try to address: they want to reduce churn, the contractual customers or subscribers who leave the company’s service. The analysis of historical data makes it possible to predict when customers will most likely go away. If you can give your account managers a monthly list of all customers with a very high churn score, you can then give them a phone call to find out the reasons of their wishes. This sounds like a simple step, but it helps a lot to retain customers.
The path to predictive analytics
These are just a few practical examples. More complex scenarios can obviously be imagined, but in general, the steps to be taken to successfully integrate this working method into your organization are the same.
1. Correct internal data
Regardless of the scenario, to make predictions, you need data relevant to your business. The size of the company determines to a large extent the complexity of the sources that you can use. Internal data is what your organization uses to record and process all of your customer data: a CRM software package, an ERP package for billing and financial processing, marketing automation, or document management. All of these internal systems contain data, which you can use to implement the predictive analytics.
2. Enrich with external data
You can enrich the internal data with external data, and look for trends and developments in the market. Consider, for example, data on branches that are developing or declining, or data from a specific target group. Big data also allows you to collect a lot of data, for example, on the topics your customers are talking about online.
3. Collect and analyze data
You can group internal and external data into a data warehouse, and analyze who your best customers are or who are the most active, who are the hardest or most loyal customers. From the analysis, you can determine how a group of clients grow or, with predictive software, how it will grow in the future. You can also update links, for example, between the size of the company and the sector, and the chances of success of a sale, which you would otherwise have missed. By combining these data with your internal data, you can establish segments and perform analyzes (predictive).
4. Establish business rules
The results of the analysis can be published in other systems so that employees and systems can anticipate and take advantage of positive opportunities or counter a downward trend. This creates a truly neutral network in your organization that reacts to a computing center as a unit from all departments, evaluating all sources within and outside your organization to determine the greatest chances for success.
Predictive analytics can answer the question “If this, then what”. You can then decide what should be done and who should be informed when a given condition is met. You have to do this for several systems, so that all the parties involved and all the systems concerned can act in the same way, in a group.
Predictive analytics solutions: make or buy?
There are two methods for integrating predictive analytics. You can opt for a shortcut, making use of all kinds of SaaS solutions that can harvest data flexibly. Everything indicates that the number of solutions for this purpose will increase considerably very rapidly. We are also seeing an increasing number of applications generating predictive leads cores by combining data from different SaaS solutions.
The advantage of such a shortcut is that it will probably allow you to get to work faster. On the other hand, you will have less freedom and fewer grips on the quality of your data. You will not have walked through any procedure where the state of mind, knowledge and culture in the context of real-time analytics will have changed. This does not generally benefit the sustainability of the organization.
Instead of choosing external software, you can create a data warehouse yourself and use predictive analytics software. This allows you to determine, at field level, which predictions you want to make and to make adaptations more easily in the way the lead scoring is done. You can adapt templates yourself and add sources to enhance predictive ability, re-consider the results of the predictive analytics and you are more flexible in sending the results to different stakeholders and systems. The path will usually take longer, because you will often bump against some barriers in the organization.