Passionate, curious, and results-oriented marketing research and analytics professional. Continually working to gain a deeper understanding of the customer through data analysis and consumer insights. Love generating useful insights for our clients to help them reach their strategic goals.
How are you analyzing your marketing data to tell the probable story of where your customers are headed? Don't let 2020 be the year you wish you had hindsight. Start using predictive analytics now to leverage existing customer data to make better assumptions (and decisions) about future customer activity.
As far back as 2015, Forrester Consulting B2B survey indicated that 87% of their respondents believe that predictive analytics can come as close as possible to forecasting future results, with statements that they were already using or had plans to implement these practices in the coming 12 months.
What is predictive analytics?
According to SAS, predictive analytics is “the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.”
In other words, what is future is typically past and you have all sorts of great use cases of past marketing and conversion data living in your database right now.
Though the customer journey is messy and can be difficult to predict — after all, everyone is a little different knowing that some customers may look for information about your brand through search, while others may click on social media post, an email, or seek out reviews from colleagues — predictive analytics can help you correlate all those actions into a strategy that makes sense and can be tested.
What are the top ways that marketing uses predictive analytics?
Top uses of predictive analytics for marketing include:
Defining your ideal customer profile and identifying prospects that match
Providing a common framework for decision-making between sales and marketing
Creating personalized content, offers, and campaigns for high-value customer segments
Increasing conversion rates, closed deals, and deal size
What is needed to implement a predictive analytics strategy?
Brands who are using predictive analytics understand that they want to know more than what has happened in the past; they want to also understand the relationships between current and historical data to make more informed decisions.
And you can't do that without clean data.
1. You must have clean data
Cleanliness is next to godliness, and this statement has never been truer than in predictive analytics. Why? Many platforms you can use to help you reap the benefits of this kind of strategy are absolutely useless if your data is rubbish.
Not to mention, you risk alienating future and current customers if you create and implement a strategy based on incorrect assumptions.
Predictive platforms you can use to help you reap the benefits of this kind of strategy are absolutely useless if your data is rubbish.
Make common cleansing procedures like data auditing, data de-duping, address validation, data consolidation, and merge/purge a monthly activity. These processes help companies maintain clean, accurate databases, which are essential for marketing data analysis and business success.
2. Start with a goal
The catch here is that your first step is going to be imagining and naming a goal that will feel ambiguous. This allows you to hone and segment your data sets down from the largest end game into forecasted target areas where they can have the most impact.
It also keeps you from falling prey to target fixation. The analytics should support and indicate the most reasonable, effective, or profitable outcome, not the outcome you most desire it to be. Position predictive analytics as an operations tactic, not just a technology tactic.
3. Use the right technology
Yes, the robots are coming, but in this case, they're fairly useful (and quite affordable too).
Look for a predictive platform with an open architecture — one that integrates with your CRM and marketing automation. By doing this, you won't disrupt current workflows and add more complexity to your daily activity (remember, we want to keep the data clean!).
Smart, predictive platforms should have the ability to pull from Salesforce, Hubspot, Microsoft Dynamics, or Marketo. Here are a few predictive platforms we're partial to:
- ClearStory Data
- Google Analytics (a bit less sophisticated than some but a great starting tool)
Don’t be lazy and assume that the database or technology will do all the work for you. People are still at the center of optimizing the effects of predictive analytics.
Look for a predictive platform with an open architecture — one that integrates with your CRM and marketing automation.
Use the tools and then mobilize your team to take those results and make the intuitive or not so intuitive leaps.
4. Test your hypothesis
Remember science class? You gather your information, set a control group, and then test your hypothesis. Applying predictive analytics for actionable insights works the same way.
A forecast isn’t a concrete prediction, and it remains simply an option for the future. Albeit an option supported by data models but an option nonetheless. You must test it to see what happens. Don’t let decision paralysis get you.
5. Try, Try Again
The objective of analytics is not only to understand why you lost that prospect or customer, but to prevent losing the customer before they jump ship. Take the data you get from your previous iteration, see what’s different from your previous try, and try, try again. Establish a separate goal and then start forecasting and testing outcomes. Every day you'll get more data and likely improve your chances of success as you continue to test and optimize your campaigns as a result.
Use predictive analytics from everything into insight in why customers are churning to identifying where the most effective upsell and cross-sell opportunities exist.
Whether you're using predictive analytics for improving your lead scoring, refining your segmentation for nurture campaigns, improving content distribution, or establish a more accurate prediction of lifetime value of your customers, predictive analytics is a good fit for a savvy marketing department focused on growth and success. Not sure how to get started? Contact Nifty today for a free consultation.
Read a case study from Pabst Blue Ribbon on how they’re using predictive analytics to reinvent their brand
Hear from other marketers about how predictive analytics is paying off for their marketing department