Predictive Analytics: How to Implement a Successful Model for Your Marketing Strategy

Predictive analytics is the process of using data in order to determine how likely certain outcomes are. When it comes to marketing, predictive analytics uses machine learning and a variety of datasets to make future forecasts and ultimately end up with better, more informed marketing decisions.

In this article we’ll take you through the steps you need to take in order to successfully implement predictive analytics in your marketing strategy.

Step 1: Planning

The first part of the process is to set your goals and targets. Using the S.M.A.R.T system is a good way to ensure your goals are good ones; they should be specific, measurable, achievable, realistic and time-specific.

You should then determine which data you need to achieve these goals, and carry out an audit of your current data sets and systems to ensure everything is up to scratch. At this stage it’s also important to answer questions such as where you’ll get the data required from, whether it be your own internally-generated insights or those from a third party such as a country-specific intelligence platform, for example a Mexico company database.

Step 2: Getting Started

When you’re first introducing predictive analytics into your strategy, it’s a good idea to start on a smaller scale. Test and trial more basic models first, in order to fully develop your understanding of the types of problems that might arise when you scale up to a larger, more complex model, and also to determine how accurate the predictions being made actually are.

Once you’ve been working on a smaller model for a while and feel good about what you’ve learnt from it and the skills you’ve developed, it’s time to scale up to a full model. Make sure you keep tracking and monitoring the outcomes in order to ensure it’s performing efficiently and giving reliable results.

Step 3: The Right Applications

There is a vast amount of applications now available for predictive analytics in marketing. Increasingly sophisticated tools are being developed and made readily available all the time, in particular there are those which have been developed especially for digital marketing, making the process much simpler.

There are also free tools available, such as Google Trends or the AdWords Keyword Planner, which offer historical data showing the popularity of different search terms. Looking at time-based insights such as those provided through these tools can be immensely useful for forming an effective predictive analysis.

You could also glean insights from social media platforms; tracking customer behaviour and their interactions with your brand can be particularly useful for making predictions on their future engagement with your products and how likely they are to convert. In any case, there are plenty of tools and applications to choose from and ultimately which ones you use should depend on your end goals and what you need to get out of your predictive model.

Step 4: Create Buyer Profiles

Building up a picture of your ideal customer is important for any form of marketing as it allows you to identify the specific type of company or individual you want to target, and helps you put together a strategy for doing so. Predictive models can help in this regard by developing profiles through drawing on various sources of data, whether it be the data that you’re currently holding on file for them following a sale, or data captured through their interactions with your website or social media channels.

The predictive model can take all of this data and turn it into a unique persona, allowing you to target specific segments much more effectively, based on their needs and preferences. You can offer specific deals, put the right products in front of the right people, or tailor your content to different groups.

The types of factors needed in order to make a fully-developed buyer profile are demographic, geographic, psychographic and firmographic. The latter is important for B2B activities; for example if you’re targeting French companies you might need to use a third-party resource designed for that purpose. With an in-depth telephone directory, France business intelligence will be listed, allowing you to find firmographic details such as how many employees a company has, or their financial performance.

Step 5: Better Engagement

Predictive analytics make it possible to gain a wide range of insights on customer behaviour. This allows you to have a much greater understanding of the way in which your target audience interacts with your brand online, across various devices. With this data you can determine useful points such as the types of contents that’s more likely to engage, the time of day or day of the week when your users are most active, or the types of call to action which prove to be the most successful, to name just a few.

You can also use tools such as Mixpanel to carry out analysis in order to determine rates of retention for your buyers who engaged with certain campaigns, for example.

Step 6: Increase Sales

 

Predictive models are a huge help in driving more sales as they offer great opportunities for upselling and cross-selling. They achieve this through considering factors such as whether or not a buyer profile fits a certain product or service, what the user has bought in the past (and how often), the user’s browsing history, and what similar users have bought.

Using this data, a forecast can be made about the types of products a customer will be interested in, and those that will complement their previous purchases. You can then make informed suggestions and upsell and cross-sell much more successfully.

Using a predictive analysis model for your marketing strategy can offer your business numerous benefits. While implementing it can seem like a daunting process, by breaking it down into the steps above it will hopefully be much more manageable and you’ll be able to determine exactly what your goals are, and the best way to achieve them.

As with any part of your marketing strategy, it’s best to keep things agile so that changes can be made in a timely manner, and it’s also important to keep monitoring your results closely to ensure they’re still as realistic as possible.