Predicting Revenue (and Marketing’s Value) Through Lead Generation

Share

As marketers, we face the daily challenge of proving our ROI.

Marketing teams still have to change the perception that all we do is ask for money to spend on fancy campaigns, conference exhibits, and schwag to give away.

We are experts at driving messages externally to prospects. That skillset needs to be driven internally as well by illustrating how marketing and lead generation metrics are the key to predicting future revenue.

Let me illustrate how you make the results of your marketing activities get the attention of the CEO and CFO—and allow them to confidently predict future revenue for their board of directors.

Most companies don’t begin to predict future revenue until a formal opportunity is created in their CRM platform. A weighted average of each opportunity then is added up to predict what revenue will look like in the current quarter, next quarter, and fiscal year.

The problem with that approach is that opportunities are not usually created until a prospect is well down the pipeline, often 2-3 months into the sales cycle.

A smart data-driven approach at the lead generation stages can help eliminate this 2-3 month lag on revenue prediction.

Data-Driven Approach

Big Data has been around long enough that people cringe at hearing the term. However, this data overload has enabled a clear opportunity for marketing departments. We now have access to our own set of Big Data, but the key to data-driven marketing is to find the “micro-data” needed to make decisions. Identifying this micro-data is the key to predicting future revenue well before those formal Saleforce.com opportunities start showing up.

Let’s start with the typical prospect stages in the lead generation process. Though yours may be different based on industry or maturity, it could look something like: Prospect > Marketing Qualified > Sales Accepted > Sales Qualified > Contract. Most opportunities are opened once they reach the Sales Qualified stage. However, by tracking the right metrics, the Marketing Qualified stage will give you a very good idea of what the revenue outlook looks like.

The key to starting the prediction process is getting a baseline understanding of how the typical prospect moves through your particular funnel. Best-in-class conversion rates depend on a variety of things such as your industry, how you sell the solution, your annual contract amounts, and your criteria for defining each of the stages. That said, typical B2B conversion rates fall in the ballpark of:

  • Prospect to Marketing Qualified—5-10%
  • Marketing Qualified to Sales Accepted—50-60%
  • Sales Accepted to Sales Qualified (Opportunity Entered)—40-50%
  • Sales Qualified to Contract—20-30%

For top-to-bottom funnel conversion, Marketing Qualified to Contract, expect between 5-10%. Getting these baseline metrics is just a start. To start predicting revenue, you need to track these metrics over time by cohorts.

Cohort Analysis

Predicting future revenue amounts is one thing, but the other is predicting the timing of the revenue. Herein lies the importance of cohort analysis.

Traditionally, you could count the average time between when an opportunity was opened to when it closed through standard fields in your CRM. But as mentioned earlier, this takes 2-3 months longer to predict than if you were to look at it from an upstream stage like MQL (marketing qualified lead).

Also, sales reps often have their individual comfort levels of when an opportunity shows up on the executive pipeline dashboard. That makes the opportunity to close time highly variable by sales rep. Tracking it at the MQL stage eliminates this.

One way to define your cohort is by month. You start by grouping all the MQLs into each month when the MQL was generated. You then track how each of these cohort groups move through the funnel to begin adjusting your original baseline metrics. What you should notice over time is that the conversion rates between stages improve over time. For example, your January cohort may have shown 52% of MQLs turned into SALs. You may have had a few initiatives that turned that into 61% for your April cohort.

You also continue to track the time between stages, as that number should shorten as you optimize your funnel moving strategies. You could see something like:

 

In this illustration, January’s 3.5% conversion rate means that 29 MQLs would need to be generated to close 1 deal in 193 days. If you were just starting out, you would expect both the conversion rate and the time to contract to improve as you test, learn, and implement new approaches.

Once we have established a benchmark, in this case 29, we can track that over time to see if it’s getting better or worse and whether there are any seasonality effects. We can also track the metric by the source of the MQL—whether it be inbound driven, a marketing campaign around a piece of thought leadership, or inside sales. You can also understand the stage where leads are falling off and pinpoint where you should be optimizing.

However, you will begin to level off at some point, in this case around 7.5% and about 6 months to close. This is where you can see the definitive value. Now, you need only 13 MQLs to generate the same closed contract, and you can predict that it will happen 6 months from the date of the MQL. You no longer need to wait 2-3 months for an opportunity to be added; your data-driven lead generation is out in front of it.

This MQL/ Close ratio is critical for tracking and is an easily digestible metric for people internally to see the benefit of optimization and how marketing performance can be the new predictor of future revenue.

In addition, you can apply your average contract size to quantify the value of each MQL. In this case, if you have an average contract size of $ 65,000, each MQL is worth $ 65,000 / 13 MQLs = $ 5,000.

You have now turned marketing from a cost-center measuring “costs per lead” into its true purpose—revenue generation, by quantifying “revenue per lead.”

MarketingProfs All In One

Share

Flash Forward: Social Intelligence Is Predicting the Future, Now

Share

This post is the second of a two-part series with our partner Brandwatch, in which Will McInnes, CMO of Brandwatch, examines how brands can unlock the power of social intelligence.

In the first post of this two-part series, I talked a lot about harnessing the power of hindsight. Evaluating the past by analyzing previous successes, and failures, provides brands with an honest look at what’s working. And what’s not.

This is an incredibly effective strategy when it comes to social data, to not only plan better for the future, but report accurately on the true reach and measurement of success for past marketing campaigns, events, corporate news, and much more.

Turning our attention forward, let’s discuss how to turn the invaluable data and insights from hindsight, into strategic foresight. We’ll also look at some futuristic trends that are starting to take off, and will really gain groundswell in the coming year.

Socially psychic

In recent years, social media has become the pillar of a brand’s customer service, as well as PR and marketing efforts (which goes without saying).

But is it enough to know what consumers, the media, and the public have said about your brand? I’d like to say a resounding “no!” If it’s been three hours since a potential customer, disgruntled employee, or influential reporter has tweeted, they may have likely moved on to another gripe, topic, or pop culture phenomenon. Real-time listening and responding will only get you so far. It’s all about predicting the future.

Enter the crystal ball that can be powered by social media data.

Through social data insights gained from deep listening and powerful, high-quality data, trendspotting becomes more intuitive. A slow, minor spike in mentions (even just a handful) can be a blaring siren of a more visible trend to come. Spotting these trends before they actually explode, is a brand’s passcode to infiltrate a story, prepare a response, or nip a potentially serious issue in the proverbial bud.

Having days of warning is ideal of course, but that’s not usually the case. Mere hours, even minutes, can mean the difference between issuing an ignorant and potentially damaging Tweet, and appearing calm, cool, and collected. And most importantly, knowledgeable.

Social intelligence provides the opportunity to get ahead. We need to move toward a predictive minded strategy to use analytics and data to determine “what is going to happen.”

Everything I’ve just described is essentially the backbone of predictive analytics. Simply defined, it’s using past data and analysis to plan for and predict the future. Beyond campaign planning, changing up business decisions based on trendspotting or past performance, predictive analytics also allow brands to delve into the shallow waters of identifying social media users with intent to purchase.

But first, infiltrate the whitespace

Whitespace conversations are those uncategorized, unbranded discussions online that relate to a brand’s industry – without specifically including any mention of a brand or even its competitors as the case may be.

Through social listening, the first necessary step in a brand’s foray into predictive analytics, you can determine demographics, geo-location, even influence of a social media user, another brand, media outlet, basically anyone. By conducting deep, refined social listening, brands can read, analyze, and understand passive data. The resulting insights can then be transformed into actionable physical insights that spark an action, a response, a story. Not to mention, a way to find potential customers.

And there you go, predictive analytics is the latest technology when it comes to smartly influencing your marketing team’s decision-making and activities, and potentially even generating viable leads and opportunities for a brand’s sales team.

Imagine grasping opportunities on social from conversations that, while relevant to your industry, have nothing to do with your brand. Social data analytics offers a chance for marketing and other professionals to tap into “white space conversations.” These are the industry-related online chats that don’t mention your brand or your competitors.

How about a quick, tangible example. If a couple is already using their wedding hashtag a year before the big day along with general ones like #wedding, #thebigday, #marryingtheloveofmylife, vendors and brands can find them through whitespace discussion monitoring via social listening.

Let’s also say that the bride is tweeting using her wedding hashtag, but also talking about how she just can’t pick a caterer. If you’re a caterer, and you’ve thrown in geolocation filters, this couple is a potential customer.

Through predictive analytics and the power of real-time (and of course past) online conversations, you can advertise, market, or even engage with this social media user.

Sure, it may at first glance it may seem like an invasion of privacy. But when it comes to public data on social media pages, individuals are knowingly posting about their woes, thoughts, needs, and triumphs. Social media has created a great platform for voicing concerns, but also for soliciting help.

Brands can pinpoint true opportunities, and follow those most worthy opportunities. Social listening is a major part of the predictive analytics trend, and will continue to gain momentum and visibility as a way for brand’s to work smarter.

The key is understanding what your audience is not only saying, but also what they might say. Using analytics to determine opportunities within online conversations can lead to a converted customer, a new business venture, or making headlines with a news story.

Full-speed ahead, to the future and beyond!

Predictive analytics, identifying intent to purchase, tapping into whitespace conversations and joining the discussion (strategically of course), there’s so much to be excited about in the world of social data! So where do we go from here?

Anywhere and everywhere! The sky really is the limit.

The development of new data technology is going to be groundbreaking in the coming year. Creating valuable new ways to look at data, compare and analyze campaigns, and providing speedy, affordable data are paramount to surviving in this aggressively competitive industry.

If I can leave you with just a few things to remember when thinking about the future of social data, it’s the following:

  • Tap into your inner psychic – predicting what will happen is the only way to stay ahead of the curve
  • Look at whitespace conversations to make the most of the millions (if not billions) of online conversations
  • The future is now! Technology in social data is moving faster than ever – use it to your advantage.

When it comes to social, it’s not enough to just listen, or even listen and analyze. Using social listening and analytics platforms and technologies available today for predictive purposes, will give your brand that extra (incredibly useful) edge.

Remember, it’s not enough to know where the puck is right now. The winners are the ones that know where it’s going.

What new possibilities are there now that a business is social?  To learn more, join Will McInnes at Social Media Week London on Tuesday September 23, where we will explore how the ease of information will impact the future of work, and how brands can best benefit from the powerful social knowledge.

Social Media Week London begins on September 22. For the full event schedule and how you can join us, visit here.

Social Media Week

Share