One of the best ways to determine what will happen in the future is to look at the past.

In today’s big data-driven world, we have the ability to access and use information on how customers are interacting with our websites and social media networks. 

But if we’re capturing this information without knowing how to use it, it’s of no use.

Strategising is an important part of everything you do in business. From deploying a new marketing campaign to coming up with a timeline for a new product release, the more information you have, the better you can plan.

But first you must learn to capture, manipulate, and model that data to use as a reference point throughout the process.

What is Predictive Analytics?

Predictive analytics is the process of mining data, studying that data and using that information to forecast future events.

If you have an upcoming event and you’re planning a social media campaign to surround it, you can look at your last campaign to find out which posts were most successful.

Maybe you posted an announcement the day before the event and found it was less effective than the same announcement posted two weeks prior. By having this information, you’ll know to make your big announcement in advance rather than waiting until just before the big day.

Even if this is your first social media campaign, you can gather information to inform your efforts.

Using Facebook’s and Twitter’s built-in analytics, you can determine the times and days of the week that your customers are most likely to interact with your social media posts. When do you get the most re-tweets and shares? What type of information seems to get the biggest reaction?

Gathering Insight

Once you’ve begun using historical data, you’ll proceed with new campaigns with a new mindset.

Knowing that the results will be used to make decisions about your future campaigns will allow you to experiment, trying new methods of reaching customers. If it works with this campaign, you’ll know to incorporate it into future campaigns while removing anything that didn’t seem to get a positive response.

It’s really important to make sure you’re measuring the right data. If your goal is to add new customers to your email newsletter, your system should be set up to capture referring URLs for each of those signups.

If you’re selling a product, your site should be measuring how many customers came over to your site from your social media posts and how many made a purchase, as opposed to merely browsing.

Tools for Measuring

Tools like Google Analytics, Facebook Page Insights, and Twitter Analytics are great for measuring interaction on those sites, but you may need more advanced tools to be effective.

If you deploy an email marketing campaign, for instance, you can use a marketing campaign tool like MailChimp to manage the process.

You’ll not only be able to automate and personalize your marketing efforts, but you’ll be able to measure results, including seeing how customers are interacting with your messages.

With each new email campaign you launch, you’ll have the information necessary to make sure every email counts. You can even see the best time of day to send future emails to maximize open rates.

The Takeaway

By using historical data to predict the outcomes of your future efforts, you’ll stop wasting time and money on campaigns that don’t work. Everything you do will be much more effective, improving your brand’s ROI on each campaign.

Most importantly, with so many other brands putting data analytics to use in marketing their brands, your business needs to use these methods to ensure you remain competitive.

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Published 9 June, 2015 by Murray Newlands

Murry Newlands is CEO at Murray Newlands and a contributor to Econsultancy. You can follow him on Twitter

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Comments (1)

Pete Austin

Pete Austin, CINO at Fresh Relevance

Beware the risks of "Data Dredging "

This happens when marketers analyze their data to spot correlations (A is related to B), without previously deciding what they are looking for. If they search for a while, they are bound to find false coincidences by chance, so you can't be sure if the results are real.

The solution is to not trust research, unless the researcher has done additional work to check the result they think they found. For example repeating their calculations using next month's figures. If the result of this new test is statistically significant, then you have real evidence, but not before.
https://www.freshrelevance.com/blog/6-things-every-marketer-should-know-about-statistics

over 2 years ago

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