Can predictive bring sales and marketing together?

SUMMARY:

Marketing and sales collaboration sounds lovely – in theory. But what happens when sales gets deluged in marketing leads? Can predictive reduce the time spent qualifying leads, and perhaps uncover new ones? Infer says yes. Barb Mosher Zinck shares her chat with Infer, and evaluates their predictive approach. Are we getting closer to “sales intelligence”?

businessman-fortune-tellerKnowing which prospects and customers to focus time and effort on is critical for marketing and sales success. You can’t hit everyone; you have to hit the right ones. Predictive and AI can help.

How is the technology adapting to support sales intelligence? I spoke with one predictive sales and marketing platform vendor to get a feel for how the market is evolving.

In my discussion with Sean Zinsmeister, VP of Product Marketing at Infer, he talked about three main issues sales and marketing face.

The Inbound problem

Lead generation is the implementation of strategies to capture the attention of prospective customers. The goal is to get contact information to pass on to Sales for follow up and hopefully conversion. Successful lead generation can yield a lot of contacts, but not necessarily a lot of qualified leads.

So what happens when you are getting way too many leads coming in from Marketing? How do you know which ones to focus on? Which ones are the right ones?

Zinsmeister gave the example of one company that had too many leads pouring in, and it was taking Marketing 100 calls to generate one marketing qualified lead (MQL – a lead that’s most likely to buy). This company adopted predictive scoring and profiling to help it narrow down the best-fit prospects to follow up with and reduced the number of calls to 12 per MQL.

How does predictive scoring help? Not only does Infer look at a contact in terms of their interactions with your company (by looking at your CRM and marketing automation), it also mines the Web and other third party data looking at potentially thousands of data points, each weighted specific to the company’s requirements. Put all that profile information together, and score it and you have a better idea of which prospects are engaging more with your company at the time when they are ready to take the next step.

There are too few of the right prospects

Maybe you’ve narrowed down the best prospects, but it’s not as many as you think there are. What if you could go through your leads and identify lookalikes? Model your best prospects and customers and use that model to find more of the same?

This, Zinsmeister said, is almost like a second step. You’ve taken the time to narrow down your leads to those with the best opportunity to convert. Now you can take that information and do some analysis to figure out who else on your list fits that model. One way Infer does this is by populating a field in your CRM and using it to show similar accounts.

Zinsmeister referred to this as a conversation planner for the sales rep, helping them figure out who to focus their efforts on. Of course, finding lookalikes works equally well for accounts as it does for individual prospects.

Did I miss someone?

Are there prospects falling through the cracks? Sometimes marketing and sales focus on certain prospects or accounts, and as a result, some equally qualified leads are getting bypassed or missed altogether.

To find out if you’re dropping the ball on a potentially important prospect or account, you can set up predictive behavior models. These models look at engagement data to understand which accounts are showing a high level of activity and shift your sales development efforts towards these prospects.

Don’t black box the predictive model

Predictive capabilities are driving a lot of marketing and sales activity. Zinsmeister said that to gain true business value companies need to adapt predictive capabilities into everyday workflows.

To gain trusted adoption of predictive, you have to open the model up and let marketing and sales see what goes into creating the model. You also need to give them the ability to provide feedback that might disagree with your model. The end goal is to create a better model; one that sales and marketing have confidence using.

The danger of over-automating

Is it possible to over-automate your sales and marketing processes? Zinsmeister thinks it could happen. It’s important to start small, he said. Get the plumbing in place and build your data model, so all your data is in one place. Append and enrich your data to ensure you are developing a model that is truly actionable. Then implement a few tactics, like improving lead scoring. Test and improve and slowly move forward.

My take

Infer isn’t the only vendor offering predictive technology to marketing and sales. Other vendors focused on individual or account-based marketing also have predictive capabilities, including marketing automation vendors. Some are more advanced than others, and it’s important to look at your needs to decide the right tools to use.

What’s interesting about Infer is that it’s a platform that supports marketing and sales equally. Zinsmeister said that they see a lot more sales-led initiatives, noting that Sales is moving up the funnel and Marketing is moving down (ABM is pushing them down).

In many ways, the line between Marketing and Sales is blurring, and smart companies know they need to equip Sales with the tools needed to work with Marketing, not just rely on what gets passed over the wall. Is this the line where Sales Intelligence sits?

Image credit - Portrait Of Businessman Predicting Future With Crystal Ball © Andrey Popov - Fotolia.com.

    1. I find that the way out of “predictive” is to gather information from your website. Let’s face it if you have a website the only reason someone visits it is because they have an interest in what you are selling or offering. Obviously someone who signs up to your newsletter is better than paying anyone else or any artificial means of weeding out the interested prospects. It is the best lead generation however you are able to predict from your own database of visitors how many people are really interested by the different pages they visit. We generate our own tracking of web page visitors and are able to run our database for the year. We are able to predict the number of people who will actually contact us against a similar period over last year. In addition we have found that the MOST interested people have a pattern as follows: Home page, About Us, Contact Us. These are the actual people who visited us and purchased our services. We also track and monitor up to the minute visitors on our website so we are aware of what our current status is. I know that most people look at historic data about their websites but actually running figures against the pages they viewed can provide the best pattern to enable you to predict the time of year visitors purchase. It is a passive approach as against phoning thousands of people however the better your website does on search engines the more you will discover new sales. Works for us.

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