MarketBridge Research: The Best B2B Marketing List Providers

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Objective

We are trying to understand the best B2B lists for acquiring new customers. We are interested in lists used to conduct email, direct mail, and telephone campaigns, across small business to Large Enterprise. We are asking B2B marketers to rate the effectiveness, accuracy, breadth, and cost of various list providers.

Once the data are collected, we will report back to survey respondents with the overall results. Where possible, we will provide crosstabs of results by industry and targeted company size. This survey is 100% free, as are the results.

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The survey will close on December 15th, 2018, and we anticipate having results by January 5th, 2019.

Episode 5: The End-to-End Strategy to Improve Customer Acquisition

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The Killer Slide Series on Data-Driven Revenue Growth

In Episode 5, MarketBridge’s CEO discusses how to improve customer acquisition and reduce CAC (customer acquisition costs) in 3 steps. With email response rates declining and sale’s lead acceptance low, today’s demand waterfall is steep. In fact, out of 10,000 prospects targeted, 4 deals might close. Businesses need two things – predictive data and content – to increase win rates by greater than 40%. Better yet, building the right data and content signals for acquisition can more than likely be done within your existing sales and marketing technology.

We’re here to help! Chat with us on improving your demand gen waterfall using predictive data and content:
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Your Leads – Rotting Away Like Week Old Fruit…

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“Big data,” “predictive analytics,” and “lead nurture” are all the buzz when it comes to go to market strategy…. So, you, not wanting to fall behind in the digital sales and marketing world invested in some pretty cool solutions that should help you gather more leads and close more deals:

  • Marketing automation tools – analyzes data to develop buyer personas and garnished some leads from your nurture campaign
  • Retargeting ads – segments prospect groups and retargeted web-visitors to drive leads
  • Dynamic web content – targets leads with the most relevant content and received a few form fills
  • CRM – Gives your reps a system to keep record of all contacts and accounts

You did everything right (according to “best practice” standards) and you rolled it out. Good news – marketing is seeing more more qualified MQLs than ever before! But wait…why aren’t you closing more deals?

 

The problem is, your MQLs don’t close deals.

Having an efficient marketing to sales handoff is critical to turn your leads into buyers. Once you identify your list of MQLS, the next step is to get them in the hands of sales people. But where did they go? Did sales follow-up?

 

Timing is critical.

Leads may be prime and interested when you initially gathered them, but they are perishable and decay at a rapid rate despite a comprehensive CRM that houses them. There are two types of decaying leads:

  1. Exhausted leads – Over touched, either by sales or marketing. Conducting more outreach could mean damaging the relationship.
  2. Perishable leads – Undertouched after passed to sales. Sales may follow-up too late, or not at all.

By ignoring the workflow (meaning, the process in which you handle leads), all of the work you’ve done to generate these leads will fall flat. Your leads will begin to decay and you won’t see changes in conversion rates.

 

Workflow is imperative.

At MarketBridge, we see this all too often. Workflow, is imperative for your team to see efforts through to revenue. Marketing to sales handoff and a standardized approach for sales execution should be built off of these 3 imperatives:

  1. Content and data gathered in one place for your sales team to use and execute on
  2. Prioritization measures in place to make sure the most important leads are communicated with in timely manner
  3. A reporting loop to understand the outcome of your efforts and know if marketing’s “hot leads” converted

 

Share your thoughts below. Do your marketing tactics often fall flat?

So, You Think You Need a New Lead Scoring Model?

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Often characterized by its blend of big data with predictive analytics, lead scoring has quickly emerged as a prevalent solution aimed to support demand generation and sales by identifying the right prospects to prioritize outreach. Over the past few years, investment in lead scoring solutions have increased exponentially as many companies have shifted resources to take advantage of the troves of customer data sitting in their information systems. Yet, with speed to stand-up a solution and technicality involved, these initiatives frequently fail, and more recently several clients have come to our MarketBridge team to ask us to ‘fix’ their lead scoring models.Why do some lead scoring models fail?

Is there something inherently flawed with how our customers build lead scoring models?

In every case of failed lead scoring, we’ve seen the same challenge – a broken lead scoring platform – either their data, systems or processes. A working lead scoring model has these component done right.

1) Clearly Enforced Data Requirements

A lead scoring model is only as good as the data you put into it. Clients seeking actionable and accurate lead scores are in for a rude awakening if they employ poor data practices. Establishing clear data requirements is critical for customers who manage leads from multiple sources. Basic data requirements include:

  • Which fields are required?
  • Which values will be accepted?
  • In what structure will the data be delivered?

This lack of basic requirements typically results in heavy manual intervention and manipulation of files before they can ever be digested by a lead scoring model, and they expose the entire lead pipeline to human error. But, of those clients who did establish basic data requirements, they were rarely enforced, which resulted in many leads running through lead scoring with incomplete or incorrect data.

Poor data requirements is the number one lead scoring challenge we have solved for our clients. And because of this, we often see good leads not making it to sales due to missing data or sometimes sales reps receive MQLS but don’t receive the contextual information to know how to follow-up. All of the data points and variables connected to that lead need to transfer to the sales team.

2) Sales Visibility and Understanding

Lead Scoring models often use a large number of valuable inputs when determining the score of any given lead. The score itself is a critical indicator to help sales reps identify who to contact first, but alone it can be less valuable for companies with a large catalog of products servicing a myriad of global industries. Without additional context, sales reps don’t have confidence in the scores and frequently chose not to use them. A lot of emphasis is placed on lead scoring to provide the ‘Who’ in ‘Who do I contact next?’, however, the ‘Why’ is equally important – ‘Why does this contact have a higher lead score? Why are they being prioritized? ‘Fit’ and ‘Engagement’ information needs to flow from marketing automation and into the hands of Sales Reps to provide context to why these are qualified prospects.

3) Performance Reporting

Lastly, I have yet to meet one executive who didn’t want to measure the performance, and more importantly, the ROI of their lead scoring investments. Performance reporting is never as simple as extracting and blending data from multiple systems. The processes that govern how sales reps track and update engagement activities performed on scored leads must be considered. For example, we recently worked with a client to scale a reporting infrastructure that could map invoices back to individual SFDC Opportunities and Marketing Automation Leads. The plot twist occurred when we ran statistics on Closed Opportunities and found that many invoices were mapping to Open Opportunities instead of Closed. It turns out most sales reps were closing opportunities and not updating the CRM, which created a mismatch in reporting results. These processes for tracking activity must be carefully defined and enforced if your organization wants to clearly measure the ROI of lead scoring initiatives.

5 Reasons Why You Should Never Use “Black Box” Lead Scoring

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We see it constantly–analytics vendors who claim to offer lead scoring services that will increase your conversion rates enormously and leave you begging for more. These companies typically promise a quick and easy lead scoring deployment that will yield a +20% increase in conversion rates. How the vendors plan to achieve said results, however, isn’t part of their pitch, and the assurance they provide you with is enough that you may just hire them. But–when it’s time to measure the effectiveness of their work, you are disappointed by lackluster results. And, rightfully so.

The term “black box” refers having no visibility into how an analytics vendor derives the lead scores they put in place for you. What variables are factored into a lead’s score and how are variables are weighted are complete unknowns. But, what isn’t a mystery is that the nuances of your business were not considered when the lead scoring model was built.

Without a crucial understanding of how your business works, the model fails to predict which leads are worth your time. As a result, you’re left looking just about as bad as your lead scoring outcome. By understanding what makes “black box” lead scoring under-perform expectations, you will be able to avoid wasting your budget and your sales reps’ time on dead-end leads.

Five reasons now to use “black box” lead scoring vendors:

1. Everything (including the kitchen sink) ends up in your scoring model

Many lead scoring vendors are confident they can churn out a quick lead scoring model without being accountable for their models’ performance. They plot the easiest path to the end of the engagement. “Why not throw all of their data into a model and see what sticks?” the vendors ask themselves. After all, that is the quickest way to deliver scores (any old scores) to the client and be done with the engagement. Minimal work for them, minimal results for you.

The obvious problem to this approach is that the scores are not, in all likelihood, accurate because the business rationale for inputs into the model haven’t been given a second thought. Anybody can slap together a regression analysis and hope for the best. But, true lead scoring success comes from careful consideration of model inputs.

2. Derived variables don’t make the cut

Just as “black box” vendors throw every variable into the model to see what sticks, they also skip a vital modeling step: deriving new and meaningful variables that have significant predictive power. As a result, they miss the hidden relationships among variables, which leads the scoring model astray.

Take, for example, a hypothetical software company that offers its customers an advanced version of its famous software. The company is desperate for incremental revenue and wants a lead scoring model to determine which leads are most likely to upgrade to the advanced product. A “black box” vendor may design a lead scoring model that uses all available fields in the software company’s database; but they would have missed a crucial relationship by not deriving a new variable: The amount of time existing customers have used the original product. Those customers with longer “product tenure” may be more likely to upgrade to the advanced product. Without this new predictive variable, the lead scoring model will most likely miss those customers and may be less powerful and less accurate.

3. Industry-specific third-party data is overlooked

In addition to company engagement data, which is incredibly important to lead scoring, 3rd party data such as company “firmographics” (company size, number of employees, etc.) and intent data help to create much more robust and accurate lead scoring models. Firmographics are often great indicators for customer preferences and likelihood to purchase. Likewise, intent data allows companies to zero in on those customers that are genuinely interested in purchasing soon.

Unfortunately, the typical lead scoring vendor may not add industry-specific third-party data that is often invaluable. By skipping out on adding this incredibly valuable data, companies will experience sub-par lead scoring performance, which will, over time, lead to large amounts of incremental revenue lost.

4. Reliance on simple business rules produces unreliable scores

Sometimes, vendors may not consider leveraging the power of predictive analytics; they will simply rely on quick data analyses to inform a few business rules in their effort to build you a “best practices” lead scoring model. Although many business-rules based lead scoring models lead to higher conversion rates and subsequent incremental revenue, it’s vital to use them in tandem with predictive models to ensure a high degree of accuracy.

With a predictive model designed for lead scoring, your results and scores will typically be much more accurate at predicting which customers are most likely to purchase. A popular analogy that compares the two methods goes something like this: Predictive models are to a scalpel, as business rules are to a chainsaw. You can get a more accurate and precise picture of your customers’ likelihood to purchase with predictive analytics.

5. Your three-year-old could have designed a better quality model

Model design is one of the important, if not most important, elements of a successful lead scoring model. One of the main problems we see is a lack of sensitivity to factors such as seasonality bias and survivorship bias. Without understanding the biases in the data and a careful consideration of how to deal with these biases, a “black box” lead scoring vendor will quickly create a model that will put you on the path toward mediocrity.

It is, of course, impossible to know that a vendor will be sensitive to proper model design that will avoid these bias issues; however, a step you can take to better gauge if this is the case is to ask. Determine if the vendor adequately describes their methodology for dealing with bias. If it sounds reasonable and carefully considered, you may have found yourself a good match for a lead scoring vendor.

Have you had any “black box” experiences like this? Tell us about them below!

Learn more about successful lead scoring here >

 

5 Tips to Improve Your Email Campaigns

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Are you seeing the desired results from your email campaigns? If you answer “no,” you’re not alone. Check and see if you’re missing any of these best practices in your marketing strategy:

  1. Create lead nurturing programs, not email marketing.

“Email marketing” oftentimes translates to sending one-off emails to customers, when companies want to promote activities such as product promotions, an event, or sales.

“Lead nurturing” is a process through which a company develops relationships with buyers at each stage of the buyer’s journey to educate and push leads through the sales funnel and eventually connect qualified leads to sales.

Lead nurturing programs encompass but are not limited to strategic email campaigns. These campaigns may last weeks, or even months. It’s important to keep the channel of communication open with prospects, even if they’re not looking to purchase immediately. According to Marketing Donut, “63% of people requesting information on your company today will not purchase for at least three months—20% will take more than 12 months to buy.” A lead nurturing program allows you to keep your brand top-of-mind for the prospect, and can potentially accelerate the decision process.

  1. Make sure you are targeting the right audience and segmenting email recipients by the stages of the buyer’s journey.

Generic emails that are sent out to broad audiences generally do not perform well, in terms of both CTRs and conversion rates, because of a variety of factors. Users are less likely to interact with email blasts, with content that may not be relevant to them.

Lead nurturing programs address this issue by identifying and creating targeted content for different industries and/or different personas, for each stage of the buyer’s journey. Leverage all of the information you have—for existing customers, use purchase history, buying preferences, and past behaviors to segment your audiences.

  1. Improve your campaigns with personalized content.

Every effort should be made to send relevant information to your prospects. Personalized content, based on information such as your prospect’s industry, role, or other profile attributes will increase your campaign’s likelihood of success. According to Salesforce, “personalized emails improve click-through-rates by 14%.”[i]

  1. Include one clear call-to-action per email.

Each email should have one and only one call-to-action (CTA). Having a singular CTA increases the chances that your lead will engage in the right activity and progress to the next stage of the buyer’s journey.

The CTA could be a webinar sign-up, a white paper download, a click-through to a blog post, or another interaction with other types of content marketing assets.

When appropriate, personalize your calls-to-action. According to Hubspot, personalized CTAs drive 42% higher conversion rate than generic CTAs. [ii]

  1. Track all lead activities, not just email clicks.

Email opens and click-through rates do not accurately indicate the success of an email campaign on their own. Your campaign should not only track email activity, but also use those activities to score your leads to identify the prospect’s stage in the buying cycle and determine whether or not the prospect is sales-ready.

Key metrics to track for lead nurturing include: unsubscribe rates, conversion rates, time-to-customer conversion, cost of customer acquisition, and customer lifetime value.

By creating lead nurture programs with targeted audiences, personalized content, and clear calls-to-action, your email campaigns will be more likely to resonate with your audiences. In addition, tracking your lead throughout your email campaigns will enable you to identify when to pass leads from marketing to sales.

Interested in learning more? Read our whitepaper on how to transform leads into customers with next generation lead nurturing.

[i] http://www.pardot.com/infographic/the-basics-of-lead-nurturing-infographic/

[ii] http://blog.hubspot.com/marketing/personalized-calls-to-action-convert-better-data

3 Essential Things You Need to Know About B2B Predictive Lead Scoring (Part 3)

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Over the last few years there has been a significant rise in the number of B2B sales and marketing organizations moving from traditional MAP and point-based lead scoring to predictive lead scoring. Many of these early adopters are now two or more years into using predictive lead scoring, and a number of common challenges and important lessons are starting to emerge.

Whether you are just now looking to make the leap to predictive analytics or are already down the path and in need of a reset, here are three essential things you need to consider:

  1. Driving adoption with your sales force

The most common reason that predictive lead scoring fails is not because the model or the underlying data is bad, but because the model outputs are not fully trusted or accepted by the sales force. Most experienced sales professionals take pride in their ability to distinguish good leads from bad ones, and asking them to put their faith in the outputs of a statistical model, which they’ve had little to no part in developing, is often too much to ask. No predictive model is perfect, and it never takes long for a skeptical sales rep to find a handful of reasons to affirm their initial doubt. Those negative first impressions are usually extremely difficult to recover from.

The most effective way to address this issue is to engage your sales force early and often in the model development process. An important first step is to gather from your reps what they consider to be the most important factors when assessing the quality of a lead. It’s imperative for them to understand that the model development is an ongoing collaborative process, in which many of their initial assumptions with be tested and validated with data, and there will invariably be findings that challenge the conventional wisdom inside the company. There will also be new factors that emerge that have never been considered. Encouraging direct dialogue with the sales force and making them an active participants in the process is critical to building trust and driving adoption.

  1. Aligning to the unique selling processes of the business

For lead scoring, most large B2B companies require a level of customization to account for the unique selling processes and complexities of their business. In our work, we’ve found the most effective solutions use a multi-level scoring approach that can be easily adapted to meet the unique needs of any B2B sales force. In that model, there are three levels of scoring that take place

  • Account – At the highest level, each account is scored based on the likelihood that it will buy one or more of the company’s products. The overall account score is based on attributes and behaviors associated with the account (e.g., industry, revenue growth), and also incorporates the quality of all contacts and opportunities associated with the account. In many cases, large accounts will need to be decomposed into separate buying units to accurately reflect where buying decisions are made inside the company.
  • Contact – Inside the account, all contacts are scored based on their likelihood to buy one or more of the company’s products. The contact scores are primarily driven by attributes and behaviors of the contact (e.g., job title, campaign response) but they can also inherit attributes of the account (e.g., industry) if those variables are found to be strong predictors of likelihood to buy.
  • Opportunity – The opportunity score goes one level deeper to predict the likelihood that an account or contact will buy a specific product. This model is particularly useful for sales reps that are focused on driving sales for a specific product or group of products.
  1. Delivering actionable intelligence

Many sales and marketing practitioners have learned the hard way that lead scoring on its own is not enough. In order for a sales rep to have a well-informed and productive conversation with the buyer, they need to know why the lead is scored high (e.g., recent whitepaper download, priority vertical), what products and services they are most likely to buy, and what content is most likely to move them forward in the buying process. Many lead scoring solutions fall short by only providing a summary lead score with little context around what behaviors or attributes contributed to that score.

A better approach would be to provide a short list of reason codes in the user interface for the sales rep. The reason codes should be organized into “fit” and “engagement” variables. The “fit” variables assess the lead quality based on attributes of the buyer and/or the account (e.g., industry, job title), while the “engagement” variables assess lead quality based on specific behaviors observed with the account or buyer (e.g., campaign response, purchase behavior). Having that type of information on the account at their fingertips, without having to do in-depth research on the account, allows sales rep to be more productive and effective with their time.

Learn more >

Why Most Lead Scoring Systems Fail to Accelerate Sales: Part 4

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Continuing with our Digitally Enabled Sales Rep series, today we’re discussing predictive analytics and lead scoring.

Today’s seller is able to target and reach out to far more prospects than ever in the past. Tools like LinkedIn have created the ability to dig into any account to identify and reach out to almost all potential buyers. Data providers allow seamless purchasing of lists dropped right into your CRM. All of these new lead sources have increased the total universe of potential customers and leads which a sales person can access. With this comes a ton of opportunity for significantly greater sales productivity.

However, most sales people don’t effectively prioritize their activities and are wired to treat all opportunities the same, which means they can waste a lot of time on the wrong accounts/contacts. The larger volume makes it difficult to prioritize accounts, leads, and contacts. In order to be successful, it is necessary to focus precious sales time on the right opportunities.

Most companies are trying to reduce this burden on the sales rep by implementing lead scoring (if you are interested in this topic, check our Essential Guide to Lead Scoring.) However, most companies are finding out that lead scoring is not enough.

First – Many lead scores are pretty rudimentary and do not use the breadth of data that can produce a more accurate and predictive result. Lead scores can also be limiting in that the quality of the company target, contact, and lead is packed into the same score. Because a sales person cannot really understand the score, they don’t trust it and use it.

Second – lead scoring is only as good as the behavior it creates in the sales rep. A score dropped into your CRM without an easy way to support and influence the behavior of the sales rep means the score will sit unused. Enabling the sales rep to use the score and transform a scored lead into a paying customer is key. Most organizations “under-club” this element of scoring.

Predictive modeling can help prioritize the contact most likely to take a call or buy your product/service. Lead scoring is necessary to help prioritize sales focus, but it is simply not enough. The best performing sales teams are going a step further and applying predictive analytics across the entire customer lifecycle – from prospect acquisition, to customer nurturing and cross-sell/upsell. The teams are also delivering the scoring to the sales teams using “designed for purpose” user interface technologies to enable the sales rep to take action and drive the ultimate result, more sales.

MarketBridge has a ton of great resources on using predictive analytics to improve sales performance. Check these resources out here and check out part 5 in this series!

Demand Generation and Lead Generation: Not the Same!

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We often hear marketers use the terms ‘demand generation’ and ‘lead generation’ interchangeably. It makes sense at a high level: both activities are performed with the goal of driving new leads. However, there are distinct differences between the two.

In the simplest of terms, lead generation is just one type of demand generation. While the two activities are related, many marketers don’t understand that you cannot do lead generation without a strategic demand generation program in place first. Understanding this relationship is key to achieving objectives, especially as marketers are increasingly expected to drive revenue rather than just leads.

To make matters even more complicated, the explosion of marketing technology also confuses the two terms. Today’s marketers are getting so specialized that their functional activities can be attributed to a distinct part of the funnel. Many marketers believe that demand generation is solely a top-of-funnel activity, but that’s no longer the case. McKinsey & Company found that,

“The buyer’s journey is no longer linear. It is a continuous loop from research to evaluation to purchase, which now includes the sharing of the purchase experience. This influences repurchase rates and additional purchases from other buyers in their own research phase.”

Demand gen programs touch every point of the conversion and sales cycle, but still, many organizations tend to skip demand generation activities because of limited budget and pressure to see an immediate ROI. While it is absolutely important that today’s marketers prove the value of their activities in the form of sourced revenue, it’s important not to forget the principles of marketing. If your goal is to generate marketing qualified leads, the best place to start is demand generation. Let’s dive into the specifics of demand generation and lead generation.

Demand Generation

Demand generation can be defined as all marketing activities that create demand or awareness about your product or service, company and industry. Hubspot refers to demand generation as the “umbrella of marketing programs within an organization.” An effective demand generation strategy not only increases brand awareness, but also opens the door for sales territory expansion and re-engagement of existing customers. These programs enable a measurable buzz in the market through marketing centric activities.

Activities that are found under the demand generation umbrella are content or thought leadership driven with the goal of establishing your company as a leader. Demand generation content is not gated, and exists to establish a relationship with prospects and customers. Examples include:

  • A PPC Campaign
  • A product video hosted on YouTube
  • A Booth at an industry event

Some of these activities may be considered lead gen activities at your company, and it’s true that they could be – but they shouldn’t be. Read on to find out how lead generation differs.

Lead Generation

Marketo defines lead generation as the marketing process of stimulating and capturing interest in a product or service for the purpose of developing sales pipeline. With the shift in the buying process and technology, there is an increase importance for marketers to focus on lead generation functions. Lead generation strategy should focus on the collection of leads usually in exchange for content using tools like forms and website tracking software. These leads would then be added to your company’s database for further marketing or sales follow-up.

Types of activities that are found under the demand generation umbrella would include:

  • A form ahead of your whitepaper or webinar
  • A form on your PPC landing page
  • A form kept at your booth in an industry event

Therefore, while demand generation establishes your company as a leader, lead generation is the act of collecting information in exchange for content as a result of that demand. You can’t effectively do B without A. When you get to the lead generation portion of your demand generation strategy, your audience will be more willing to exchange information for valuable content, and thus more likely to become a customer.

Differentiating the two can be confusing, and with the rise in digital technology it only increases the complexity around the two topics. In conclusion, if you are tasked with developing lead nurture programs in effort to provide higher qualified leads to sales, the first place to start that process is to look at your overall demand gen activities.