How to Evolve Your Prospect Management in a Predictive World

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Posted: January 27, 2016 | Lead Management

It used to be that prospect management meant dolling out “Glengarry Leads” to add to your Rolodex, keeping a spreadsheet of top prospects, or most recently populating an empty CRM database with leads that seem to “look good.” But as the predictive sales and marketing landscape has flourished (with new vendors emerging and $242 million in venture capital funding last year alone), old school definitions of prospect management are quickly losing their relevancy. Today, you can pinpoint the best net new prospects with unprecedented precision–a better way to feed hungry salespeople and avoid forfeiting deals you don’t know about to your competition.

The current state of disconnect

Customer data is exploding, and it’s now spread across several different systems inside and outside a company. While this is essentially a good sign that your lead generation is working, unfortunately, when data systems don’t talk to each other or track prospect activity in the same way it can feel nearly impossible to interpret each customer breadcrumb for valuable insight. This is one reason why some companies still rely on “spray and pray” marketing techniques. Another is that the data in front of them is limited to very basic information (and can be stuck in closed systems), making effective personalization a daunting challenge.

In addition, many businesses still struggle with conflicting vocabulary across various parts of the organization that each have their own system of record. The sales team probably speaks the CRM language of “opportunities” and “closed/won” deals, while the marketing team lives and breathes “demand waterfall” terminology like MQLs (marketing qualified leads) and SQLs (sales qualified leads). This disparity plays a role in the common disconnect between sales and marketing over what defines a good lead, and breeds confusion and friction by requiring employees to translate their work for each other.

But solutions are emerging to help solve all of this and change the way we manage prospects—predictive solutions. So what does it mean for marketers? Here are three primary ways that smart marketers can reinvent their approach:

1. Build a portfolio of ideal customer groups

This step entails looking across all the attributes of your current and future customers, and finding ways to slice and dice your full prospect universe into easy to describe, easy to target profiles. As account-based marketing gains steam, it’s important to note that this universe encompasses more than your current CRM (customer relationship management) or marketing automation platform (MAP) customer database; it includes external signals and net new accounts you should be targeting.

Rather than honing in on 3-4 rigid “personas” for individual buyers or attempting to target huge groups based on geography or company size, advanced segmentation leverages all of your available customer data to get more granular. This approach lets you create a portfolio of key customer profiles. As described by AgilOne’s Omer Artun in his book, Predictive Marketing: “Marketers need to recognize that different groups of customers have different value and different behaviors and take different actions based on these distinct customer segments.”

There are many ways to understand your portfolio of prospects beyond their common demographic and firmographic characteristics. Now you can look at more dynamic “technographic” traits, like what applications and platforms their company is currently using, or you can pull in predictive scores that track how good of a fit they are for your product and where they are in their buying journey. Furthermore, you can measure lead effort, such as how many touches a contact has received from your sales team.

With all of this fluid insight, you can define more narrow, yet more flexible and descriptive prospect profiles–i.e. low effort, high fit scores in APAC or SMB accounts with contacts with external behaviors–and provide a common vocabulary that spans all of your go-to-market groups and their programs. This makes it easier to navigate your marketable universe and allows for more meaningful, personalized interactions with prospects. This is key because it’s not enough to just decide who your top prospects are. To be successful, especially with predictive-driven marketing, you need effective techniques to segment and target these groups.

2. Activate your customers’ journeys

The next step is to figure out which tactics or campaigns will do the best job of accelerating prospects along on their path to purchase. For some profiles, this means engaging sales development reps and monitoring their service level agreements. Maybe you kick off an automated workflow in a sales development automation app for email communications. For other groups, it’s about getting the contacts to engage with your marketing programs by determining the content that’s most likely to draw them in. Perhaps you try a custom content marketing or mobile marketing program targeted at the accounts you care most about.

Marketing is all about doing sales at scale, so regardless of which prospect group you’re focusing on, it’s always important to provide the sales team with air cover. But to do this right with limited resources, you’ve got to nail your focus and ensure that you’re aligning effort with impact vs. trying to boil the ocean. Predictive models can help guide you by recommending the right kind of engagement–whether that’s a sales or marketing task–with the right message at the right time. Predictive scoring uses both the valuable internal data from your CRM and MAP systems plus thousands of external signals from a variety of data sources outside your company. It also uses machine learning to look at all kinds of combinations in the data that humans could never grok on their own–taking the guesswork out of the equation.

3. Expand with confidence

After implementing predictive sales and marketing strategies, you might start to feel like you’re exhausting your existing market. The logical next step is to rinse and repeat, but you don’t need to start from scratch. Artificial intelligence (AI) can help you determine where to go next and give you more insight into your lead database.

Predictive applications can go out and find more people that are a fit for your business based on the criteria you identified back in step one. Just as consumer services like Pandora, Waze and Amazon deliver increasingly personalized recommendations over time, B2B marketers can leverage the same virtuous cycle. New models can hone in on the most important feedback mechanisms and time triggers for your marketing programs and help to make them more and more impactful.

For example, you can use data science to automatically calculate pipeline metrics and determine the predictive value (i.e. projected revenue, conversion rates, and sales effort) of new market segments, as opposed to focusing on a raw metric like lead volume. And by obtaining a deeper understanding of net new prospects across a variety of signals, such as which technologies a company is leveraging in its stack, you can also use “hyper-segmentation” to deepen your level of personalization and construct more meaningful communications.

The Brains of Sales and Marketing Operations

The companies that have an edge on their competition in the arms race for data are those that find ways to augment and optimize their sales and marketing stack around these new predictive methodologies. Prospect management can become so effective that it takes on a role as the brains of sales and marketing. It’s no longer a mundane task that’s seen as a barrier to entry for CRM or marketing automation systems. Rather, it’s a wholly new approach that automatically interprets data, makes that information easy to act on, and removes much of the unnecessary manual intervention required in marketing operations today.

How do you think a predictive solution would impact your marketing?  Will you be implementing any this year? I’d love to hear in the comments section below.

 

Sean is the Senior Director of Product Marketing at Infer and crafts the positioning, messaging and overall go-to-market strategy for Infer’s trove of next-generation predictive analytic models. Once a satisfied Infer customer himself, Sean joined Infer from Nitro, where he developed and led an award-winning global marketing team.

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