A Q&A on the Future of Data-Driven Marketing

Data drives better business decisions. It’s impacting how marketing and sales activities are planned and executed. But what are some of the most common mistakes being made when collecting and assessing all this data? Rob Weedn digs deeper:

How is data-driven marketing impacting the way B2B marketers are approaching their sales and marketing efforts? How have you seen it evolve over the last few years?

Today, data powers the marketing and sales machine, yet, most B2B companies haven’t raised their standards when it comes to data. They lack complete, accurate, contact and account details for their target buyers. They’re not reaching their total audience. They lack the ability to drive conversions because their data isn’t relevant, and they can’t track multi-channel marketing and sales campaigns effectively.

Where data-driven marketing is going: Data, systems, and processes all geared to serve and fulfill the buyer journey–with the ability to track all the data across that journey, including your engagement points, broader buyer intent by account and demand center, as well as broader social and firmographic insights.

What are the common pain points of B2B marketers when it comes to demand gen today? What are some of the common mistakes you see CMOs and CSOs make when trying to use insights from data to drive conversions?

There are a couple of pain points and mistakes we see pretty consistently:

1.  B2B Marketers don’t know their total audience: Some companies are trying to jump right into account-based marketing (ABM) without first measuring their total audience, or total available market (TAM). This is an issue I recently blogged about too. The problem is that then you’re flying blind. You need to know how many accounts, and individuals–since people buy from people–are potentially a good fit. Without knowing your TAM, you cannot scope ABM effectively. If you under- or over-estimate the number of demand units, you risk budget issues and missing revenue targets.
2. The ripple effect of bad/missing data: Most companies’ CRMs and marketing automation systems are missing 80% of their total audience, and the data they do have is up to 50% wrong because data decays at an alarming rate.

Incomplete, inaccurate data negatively affects teams in several ways:

1. Low conversion rates: While marketers may think they have excellent data coverage for ABM, as they start running campaigns, they aren’t successful.
2. Marketing-sales misalignment: Sales may complain that they’re wasting time researching direct-dial phones or that titles are wrong so they’re not reaching target buyers. That breeds mistrust and you end up with disparate systems, which hampers your ability to track across the customer journey.
3. Lack of multi-channel contactability: You can’t “surround sound” your target audience by phone/social/email/postal mail and run effective multi-channel campaigns across demand gen and sales.
4. Bad business decisions: Data issues can become systemic. If you’re making strategic decisions based on the incomplete, inaccurate data across your martech stack, they’ll be bad decisions.

This is why we firmly believe that data needs to be enriched and validated, on-demand so it’s fresh and accurate right when you’re going to use it. We’ve benchmarked data decay ourselves. We ran a campaign with validated emails in December and had 99% deliverability. We then ran a second campaign to the same list a month later and deliverability dropped by 12.5%. Too many marketing teams are making this same mistake.

How can data help B2B marketers create a comprehensive buyer persona for their brand? Can you share some tips? How can sales and marketing work more closely to develop the ideal persona?

Having worked with teams of all sizes across many industries, we’ve found that close marketing-sales collaboration is key when developing buyer personas. It’s also important for teams to get as granular a definition as possible. Sales is likely interacting more closely with prospects and customers on a daily basis, while marketing may have more high-level analysis and insights into what’s driving conversions, so together, given the proper tools, they can get very specific and really hone in on their best targets.

In terms of granularity, teams should look beyond just a title or area of responsibility such as “Information Technology” or “IT Operations Manager” and dig into the specific skills those people have and the technology they use that make them a good fit, such as “IT Service Management”, “ITIL”, or “VMware”.

What is TAM analysis? Could you give us some interesting use cases on how DealSignal’s approach has helped marketers drive business outcomes more effectively?

TAM analysis is a process of measuring the total available audience (or total available market) for a specific ideal customer profile + target persona — basically, measuring how many people you can potentially sell your product or service to, given your target buyer. If you’re following SiriusDecisions’ Demand Unit Waterfall model, it’s that first step to define and measure your Target Demand.

Once you’ve defined your target personas and ideal customer profile (ICP), a TAM analysis helps you in two major ways, especially if you’re doing ABM:

1. Identify gaps in your CRM data coverage: By comparing your TAM against the data in your CRM or marketing automation system, you can evaluate how many target buyers you might be missing. We find that most companies only have about 20% coverage!
2. Fill the gap: contact discovery: You can then use contact discovery to improve your TAM coverage and quickly map your ABM account lists.

We have helped customers fine-tune their target personas by 5-10 micro-filters to either slightly expand their total audience or provide more focus and it has resulted in a 3-5x conversion increase for their ABM campaigns.

Do you feel that sales teams don’t (yet) fully realize the impact of data-driven decision making? What are the 3 things you would say to them in this context and how can CMOs help CSOs drive this culture?

It’s a gross generalization, but salespeople tend to be impatient and move quickly. They’re under a lot of pressure, so if results aren’t immediate, they move on. I think that with the rise of salestech, specifically AI sales solutions, sales teams are starting to see more value from data-driven approaches instead of relying on their isolated experience and what seemed to work well for their last big deal. Here’s what I would say:

1. Lead from the top: C-level leaders need to set the example for sales-marketing alignment and creating a data-driven culture. They need to bring everyone to the table regularly to share data about what’s converting and what isn’t, and then ask why. The first two points are purely data-driven, but the third opens a dialog for sharing qualitative insights from both teams’ interactions with prospects and customers, which helps everyone fine-tune and improve their approaches.
2. Collaborate on defining target buyers: To be successful, teams need a crystal clear, shared understanding of their target personas, ideal customer profile, and KPIs. That alignment will help avoid time- and budget-wasting one-off requests, eliminate debate over lead qualification, and will ensure that the whole team is working towards the same goals. Along those lines, teams should celebrate milestones jointly to foster team spirit.
3. Being data-driven requires good data: As I alluded to earlier, it’s garbage in, garbage out. If marketing and/or sales have incomplete, inaccurate account and contact data, they cannot make effective data-driven decisions.

Experts talk about the gap between good data collection and the ability to use this data to generate insights that are actionable. What are your tips to marketers for building a practical and sustainable roadmap to activate data strategically?

If we talk about account data or contact data, it’s usually gathered from web form fills or tradeshow badge scans–sources that are necessarily limited, as no one wants to burden prospects (or negatively impact their conversion rates) with long, detailed forms. The other data source is typically CRM data entry by Sales or Marketing staff. So again, you’re left with incomplete, and often inaccurate data, which doesn’t generate helpful insights.

Using data strategically means having the right volume of clean data in a centralized location so that it can be analyzed effectively, by people talented enough do to so, and can be activated, as you said, for use in data-driven systems.

So I would suggest a few things:

1. Data enrichment: Limit web forms to the bare necessities and use on-demand data enrichment to augment your CRM and marketing automation data so that you have an accurate, complete, and up-to-date picture of your customer base, target accounts, contacts and prospects.
2. Full Contactability: Let’s not overlook that most CRM data is not actionable, it lacks the critical information that marketing and sales teams need to engage potential customers across channels–verified emails, verified corporate and direct phones, all social URLs, demographics and recent news/posts about the account or by the contact. To drive personalization, you need a comprehensive contact profile. To drive action, you need full contactability across communication channels. Both are critical and need to be accurate, updated frequently, and verified.
3. Relevance: Use a relevance score or lead score that prioritizes leads and contacts based on how closely they match your ideal buyer personas…routes to the most appropriate person or process (nurture, SDR or field), then track and surface the relevant criteria so that you can measure conversions against the scores & criteria, and so that Sales can start the conversation with insights from the criteria.
4. Evaluate your CRM health: Everyone knows their database is less than perfect, but many companies don’t want to undertake the effort to quantify the scope of the issue or they’re scared to delete anyone “just in case”. Call it Spring Cleaning, a very late New Year’s Resolution or a GDPR necessity, but make 2018 the year you clean up your database.
5. Avoid building silos: If your marketing, sales, ops, and finance teams are using siloed systems with very different data definitions you’re going to have issues. That doesn’t mean you need one mega solution, but, build your martech/salestech stack with an eye toward interoperability, easy integrations and data sharing. And be sure you hire ops professionals that can manage the systems effectively.

How do you see the increasing stringency in data management, data privacy and data protection impacting CMOs and CSOs conversion efforts, and what should they be preparing for today?

Like it or not, the new rigor will force companies to take an overdue look at their data practices and put better habits in place. Here are a few ways CMO and CSOs should be preparing:

1. Marketing and sales need to have complete, accurate, and updated information in order to control their CRM systems according to laws in the EU (GDPR), Canada (CASL), etc. They need to track Do Not Call, but they also need to track Opt-In, which, believe it or not, is not a standard field in Salesforce today!
2. Marketing, along with sales’ input, needs to regularly produce and publish high-quality content that will drive opt-ins. They also need to rethink the customer journey to drive buyers with immediate intent into the sales funnel and also nurture buyers with longer term goals into an opt-in.
3. Marketing and sales need to have better processes to track customer journeys and when an account and/or set of contacts is not engaging and/or opt-out, they need to be clearly marked and integrated across their CRM, marketing automation and other systems.

What are the technologies, innovations and trends that will impact the way enterprise marketers approach segmenting, targeting and conversion in the near future?

In the near term, we agree with SiriusDecisions’ Demand Unit Waterfall, that its critical to start by understanding your TAM–your total audience metrics–and then understand which accounts are showing intent to buy, where the demand centers are located, and then quickly be able to run marketing and sales outreach to those doing evaluations.

Also in the near term, we want to see CRM integration become much more streamlined so companies can create a database with their total audience and then keep that information updated, accurate, and verified on a monthly basis. We also see value in tools that can integrate across the full martech and salestech stacks and keep information up-to-date and synchronized across systems, with CRM as a hub-and-spoke, rather than 5-10 point-to-point integrations.

On the longer-term horizon, honestly, we believe the future involves a rethinking of CRM; that CRM systems should be mapped to customer journeys rather than a decades-old SFA (Salesforce Automation) schema. If the core system of record could handle diverse customer journeys, track interactions across engagement points (web, syndicated content, social, email, phone, and checkout), you’d have the foundation for intelligent chatbots to assist the customer journey in very intelligent and prescriptive ways, you’d have the foundation for AI to truly start to inject itself in the customer conversation.

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