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Predictive Analytics In 2017 - Will It Pick Up?

Posted by: Rushal Patel Date: Feb 10, 2017 1:39:00 PM

Predictive analytic can help you create experiences that resonate with clients and leads. This will help you to create an effective and streamlined marketing plan that gets your startup's message across.

Have you ever marveled at the fact that Netflix seems to know exactly what to recommend in your queue whenever you load your screen and settle in for a night of eating popcorn in front of a good show or movie? You can thank predictive analytic for those smart recommendations. The reality is that you don't have to be a media giant like Netflix to take advantage of this amazing technology. In fact, startup B2B technology companies are actually the players that have the most to gain as the mechanisms behind predictive analytic becomes easier to use and adopt into pre-existing marketing and sales plans.

What We Know About Marketing Analytics

Most B2B marketers already have a pretty good understanding of the importance of marketing analytics. A survey by Regalix earlier this year revealed the following things:

  • 82 percent of B2B companies use marketing analytics tools
  • Of the 18 percent remaining, 67 percent said they would be using marketing analytics tools in next 12 months
While use of analytical tools is widespread, many B2B companies are actually behind when it comes to keeping up with the newest resources that are available to enhance analytical capabilities. What's missing? Not every company is up to speed when it comes integrating analytical technologies into their websites and customer portals.

Defining Predictive Analytics

Predictive analytics is an area of data research that's used to make predictions about unknown future events and behaviors. Predictive analytics provides a method of creating focused, targeted prospecting and lead generation. This method is ideal for use with account-based marketing because successfully following leads based on current accounts all comes down to measuring and predicting the behaviors of those accounts. Properly using predictive analytics should look something like this:
  • Create an ideal customer profile based on the data you have
  • Strategically target the right accounts and pick out the best leads within those accounts
  • Craft content and experiences tailored to the preferences and needs of those leads

Where Is The Information Used For Predictive Analytics Sourced From?

The success of predictive analytics relies heavily on the quality of the information you're able to feed into the process. Predictions are made using the following sources and methods:
  • Data mining
  • Statistics
  • Modeling
  • Machine learning
  • Artificial intelligence

Using The Information At Your Fingertips

Sourcing high-quality information to use for predictive analytics is actually a lot less intimidating than it might first appear. The reality is that today's customer relationship management (CRM) software and monitoring and administration tool (MAT) software both take in massive amounts of usable information and turn it into digestible formats that can be fed into your process for using predicative analytics. In addition, most modern ad platforms deliver tremendous amounts of information regarding user reactions and behaviors. You can use the information harnessed from these sources to conduct a predictive analysis and improve the following aspects of generating leads:
  • Target selection
  • Message personalization
  • Content quality and effectiveness

Target Account Selection

Target account selection is incredibly important for B2B startup companies that may not have the funds to pour into pursuing a wide variety of leads without at least some assurance that the efforts will pay off. Predictive analytics can take some of the risk out of throwing down marketing dollars because it offers measurable predictions regarding which accounts may be the most likely to need your services. This information can help your B2B company to identify and prioritize the right contacts. In addition, your sales team can gain a good understanding of the products those targets are looking for. Having all of this information at your fingertips can help you to craft a message that will resonate with your buyers and enhance their interactions with your company.

Predictive Pricing

Pricing is one of the big areas where B2B marketing companies make fatal flaws. Predictive analytics can help you to avoid the mistake of missing out on the chance to target the right leads with the right prices. Your company can actually use records of customer browsing and purchasing habits to adjust prices over time. In addition, you can also create very personal pricing experiences for select leads and clients. The information you accumulate via predictive analysis can be used to target different consumers using the following:
  • Exclusive discounts
  • Promotions
  • Segment-based pricing

Predictive Analytics Fosters Increased Lead Conversion

Predictive analytics is already driving so many of our behaviors without most of us realizing it. This tool is actually the most effective as it pertains to convincing us to connect with others and engage on platforms. You're actually turning yourself into a converted lead every time you view the profile of a suggested friend on Facebook or click on the photo of a suggested match on Match.com. Every mainstream social media website and commerce website essentially operates using predictive analytics. Your experiences are tailored to be enhanced for your personal preferences and behaviors based on your past actions and connections. Adopting predictive analytics for your B2B company's online presence or internal reporting can help you to wield that same level of persuasiveness.

Your B2B Company Can't Lead In 2017 Without Predictive Analytics

Every marketer wishes for a crystal ball. Predictive analytics is the next best thing. It's anticipated that 2017 will be a breakthrough year for the role of predictive analytics in B2B account-based marketing. Your B2B company simply isn't maximizing its potential for converting leads if it isn't using the information that is generated through previous sales and interactions to discover what's necessary to make your contacts close deals with you. Adding automation on top of this could help you with the data mining to find consumer behavior and pattern, which is the foundation of predictive analytics. Let automation tool makes it easier and effective for you to create a fruitful buyer's journey.

Are you interested in using the simplest method for anticipating what your clients want and measuring the future demand for what you offer? The success behind predictive analytics all comes down to using what clients have already done to predict what they're going to do next. Make 2017 the year your B2B tech startup finally gets the crystal ball you've been wishing for.

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Topics: B2B Marketing, Predictive Analytics