Winning in the BizOps Era, Part 3: Rock Stars, Gut Instinct & Zooming In on the Best Accounts

Winning in the BizOps Era, Part 3: Rock Stars, Gut Instinct, & Zooming In on the Best Accounts Rekener Account Lifecycle Management

Hi everyone. Since we started the company, we've been talking with old and new BizOps friends in the recurring revenue community about the emerging powerhouse that is the BizOps function. It’s been wonderful to hear your ideas and share a few of our own. Several of you asked us to write it all down, so we did. Heads up: It’s a bit lengthy (wicked long, actually), so it's in three parts. You can read part 1 and part 2 here on the blog. If you prefer PDFs, here are links to part 1part 2 and part 3.
– Steph

The Operational Disconnect in B2B Sales

The Good (and Not So Good) Old Days of Sales Rock Stars

20 years ago, in the days before Salesforce.com and recurring revenue models, we hired sales rock stars: people who had prior expertise and sales success in our target market. If we were building a direct sales team, we looked for a direct sales rock star. If we were pushing our product through the channel, we hired a channels rock star. We were taking a risk that the lessons learned by that expert in the past would result in future success in our business. Chances were, if you hired a rock star for your new band, you’d be cranking out the hits in no time.

Back in 1996, when I started my first company, NBX Corporation, this is exactly the approach we took in hiring our CEO. We had identified an opportunity to use Voice over Internet Protocol (VoIP) technology to disrupt the existing business telephone ecosystem using our new IP-PBX. The incumbent providers were slow-moving giant companies like Lucent (a recent spin off from the even slower and more giant AT&T), Nortel and Siemens, among others. The smartest thing I ever did as an entrepreneur was to recruit a former senior sales leader from Lucent who knew how to sell business telephone systems, both direct and through channels — a real rock star. He could see that we needed to create a reseller channel and brought on a team to make it happen. He knew what plays to run and who to hire. We were in the midst of executing his go-to-market strategy when both Cisco and 3Com, the two largest data networking companies in the world at the time, each decided to add an IP-PBX product to their portfolio. 3Com bought NBX and the rest is history.

The practice of hiring a senior sales leader hasn’t changed that much in the last twenty years. We still look for a rock star: someone with experience in our domain, and the more familiarity they have selling similar products or services to the same or a similar industry, the better. We still look for a broad range of experience with different paths to market, including inside sales, field sales, channels and partnerships. We still look for a sales leader with the ability to implement successful plays while also knowing how to bob and weave when new situations arise. We still look for someone with one or more big successes in their past, because that helps us feel even more confident that some of the star power that worked for them before will work again for us. And when we find that person, we expect them to hit the ground running, and we give them a tremendous amount of leeway to execute.

The Challenges Facing the Modern Sales Leader

Sales is unlike all other aspects of your business because it has the ultimate trailing indicator: either you hit the number or you don’t. In the old days of sales twenty years ago, the reward for hitting your number was to be treated like a rock star: you’d get paid a lot more and you’d be left alone. Only salespeople who missed their number were required to explain what happened, and a bad explanation often meant being fired.

Today we know that even the best salespeople miss their number from time to time, and most salespeople experience the frustration of coming up short now and then. We know it’s important to not only focus on the sales results as a barometer of performance, but also use data to assess the quality of the sales process itself.

Over the last 10 years, the tech industry has become more and more interested in measuring the sales process using Key Performance Indicators (KPIs) as both a leading indicator of sales projections as well as an explanation for sales results. In fact, there’s been an explosion of tools to measure and generate KPIs for every aspect of the sales process. As a result, sales leaders are no longer left alone when they hit the number. All sales leaders are expected to use data to explain how they can improve, even if the numbers are good, and their employers use that data to help less successful members of the sales team modify their approach.

So what’s going on here? It isn’t that sales leaders have become less talented in the past 20 years. Instead, the path to market is now much more complex, and the emergence of the cloud, Software-as-a-Service (SaaS) and subscription business models have revolutionized the way many tech companies operate. We can no longer rely on the rock star model: hiring a single sales leader with all the necessary product, industry and go-to-market know-how to figure out which approach will work, what people to hire and what KPIs to measure.

Further, the days are long gone when a salesperson could be expected to keep track of everything going on at a customer or prospect account in the Fortune 500. And these changes aren’t limited to high-velocity sales teams selling subscription-based SaaS products. Even if you’re not in a SaaS business — maybe you sell to the Fortune 500 with a perpetual software license model — the enterprise sales model is increasingly data-driven.

The automation of marketing and selling processes has given companies huge amounts of data about how they interact with the customer. More importantly, the evolution of the tech buying process from a top-down decision to one made by a group of stakeholders — best described in The Challenger Sale by Dixon and Adamson — has made it increasingly difficult to have an intimate knowledge of what’s happening at a prospect or customer account, even when the list of target accounts is relatively small. And because the SaaS model means that we grow these accounts by upselling and cross-selling, we’re essentially repeating these marketing and sales processes over and over throughout the account lifecycle, or the lifetime of our entire business relationship with a given customer. A single account manager, even a rock star, can’t possibly track a lifetime of account interaction given today’s sales reality:

  • We send thousands of emails to hundreds of different contacts at our accounts via increasingly sophisticated marketing automation platforms.
  • We promote multimedia content 140 characters at a time through dozens of social media channels.
  • We connect with our customers and prospects in many other ways as well, including at trade shows and meetups, via search-friendly web content and through reseller partners.
  • Salespeople are interacting with any number of buyers in a given account in order to make the sale. As noted in a The Challenger Customer by Adamson and Dixon, the average number of stakeholders involved in a purchase decision is 5.4.
  • Customer Relationship Management (CRM) software enables our sales teams to interact and manage large numbers of prospects and customers.
  • Tools for automating email sequences and outbound calls have increased our customer outreach capacity, enabling the typical inside sales rep to reach out to 100+ contacts a day.
  • Once we’ve successfully sold to a customer, we collect even more account lifecycle data, in the form of customer support tickets and activity tracked via product usage and engagement systems.

With the proliferation of these tools, the massive amount of data generated by them and the emergence of recurring revenue business models, business leaders can no longer rely on the experience of rock star sales executives to hit bookings and revenue targets. They’re expected to find the answers in the sea of data that their business is collecting every day. And this is where sales management breaks down.

As Jason Jordan writes in Cracking the Sales Management Code, today’s sales managers have become very good at capturing lots of metrics relating to the sales process, but they struggle to understand which metrics or combination of metrics are most important to driving high-level business objectives.

The Operational Disconnect — Volume vs. Value

The challenges of the modern sales leader are exacerbated by the growing disconnect between the high-level objectives that create value for the business and the metrics to measure the success of go-to-market teams, including sales, marketing and support. In Winning in the BizOps Era, Part 1, we introduced this disconnect and explored it in detail. In summary, businesses today are largely valued based on revenue generated over time in proportion to customer acquisition costs (CAC).

Businesses today are largely valued based on revenue generated over time in proportion to customer acquisition costs.

For subscription-based revenue models, including SaaS businesses, this is described by the ratio of customer lifetime value (LTV) divided by CAC, as described by Philippe Botteri in the 5 C's of SaaS Finance and David Skok in SaaS Metrics. The concept of maximizing recurring revenue over time also applies to non-subscription businesses. In fact, any business that sells software or hardware or goods or services to a given customer at multiple moments over time is looking to maximize the LTV of accounts, though the formula for calculating LTV for a non-SaaS business will vary from business to business.

While there’s growing recognition that the value of a SaaS business depends on the company’s ability to grow revenue from its accounts over time, most B2B businesses are still organized operationally into departmental silos — and so is their data. Marketing is generating leads, sales is closing deals, and support is closing tickets. These functional silos are reinforced by two key elements:

  • Software platforms optimized to help these teams achieve department-level goals, and
  • Compensation structures that reward department-level achievement.

At best, there’s collaboration between marketing, sales, and support; at worst, there’s open hostility. Silos tend to contribute to the latter.

The problems with today’s pipeline-based operational structure are outlined in detail in Winning in the BizOps Era, Part 2. In summary, B2B go-to-market teams aren’t set up to achieve these company-wide objectives and maximize recurring revenue over time, and are instead set up to optimize results at the department level, inadvertently limiting revenue growth.

B2B go-to-market teams are designed to optimize results at the department level, inadvertently limiting revenue growth.

To maximize recurring revenue over time, company-wide goals would reflect each team’s ability to contribute to one or more of the following objectives:

  • For the net new sales team, we want to “land smart,” or close net new deals in those accounts with documented attributes of customers likely to generate high LTV;
  • For the upsell team, we want to focus our account managers on the accounts most likely to expand, based on how we know accounts expand over time;
  • For the cross-sell team, we want to run targeted campaigns based on a data-driven understanding of which products are best for landing and which products are best for cross-selling; and
  • For the account management team, we want to monitor leading indicators for churn in order to keep high-LTV but at-risk accounts as ongoing customers.

In short, maximizing account LTV requires the prioritization of sales and marketing resources around the best accounts and segments to achieve these goals. Yet the operational disconnect exists because we’re not set up to do this at all. Instead, we’ve set up our business to maximize the number of activities that each team is tasked with.

  • The marketing team generates as many leads as it can by sending as many emails as possible and creating and promoting as much content as possible.
  • The sales team contacts as many net new targets as it can by making as many calls as possible and sending as many emails as possible. The same is true for upsell, cross-sell and renewal teams focused on existing accounts.
  • The customer success team closes out as many support tickets as it can, keeping as many customers as possible from churning out.
Recurring revenue businesses require more than a view into the volume of activity in each department.

Recurring revenue businesses require more than a view into the volume of activity in each department.

But doing more isn’t necessarily the same as doing well: resources are not unlimited, and not all accounts are created equal. Yet instead of supporting what the company needs to do at a high level, our structure and tools continue to support department-level goals, even when they conflict with the company’s overarching mission to drive lifetime value from accounts. What’s going on?

Don’t Ignore the Elephant in the Room

During quarters when bookings and revenue are growing and the sales team is hitting its numbers, the operational disconnect is invisible. But, inevitably, when revenue growth starts to slow and the marketing, sales and expansion teams ask, “Why is this getting harder?” the problem becomes acute. The operational disconnect is the issue, but it’s still not easy to recognize it in the moment, particularly due to departmental team and data silos. Symptoms include:

  • A slowing revenue growth rate;

  • A shrinking average selling price (ASP);

  • Increasing churn;

  • Declining win rates;

  • A decreasing LTV/CAC ratio; and

  • Lengthening payback periods for CAC.

When we see these symptoms and we’re set up operationally to drive a high volume of department-level activities, we’ve got an elephant in the room. Your instinct might say to go faster or do more, but doing more of the same thing is actually likely to make the problem worse, because you’re essentially doubling down on activities that drive up CAC without first determining how to increase LTV.

So what do we do when this happens? As with most things, there’s a bad response and a good response. Let’s explore both.

The Twin Dangers of Gut Instinct and Over-Averaging

Don’t Trust Your Gut

When we hire sales rock stars based on their prior success and expertise, we expect that experience will help them make future good decisions from the gut, especially when circumstances get tough. The problem is that relying on gut instincts is about as effective as a crapshoot. It’s possible that we’ll get it right, but the chances of getting it wrong are much greater. Consider two scenarios:

  1. A cloud-based software security start-up has had great success in the technology and financial services segments, but it soon starts to experience some of the symptoms of slowing growth. Over the weekend, the VP of Sales, desperately seeking ways to re-start growth, reads a well-researched and data-supported article in a prominent business journal describing cloud security problems faced by retail businesses. At the Monday morning executive meeting, she convinces the team to focus all sales and marketing resources on retail.

    This sounds like a flimsy rationale for allocating sales and marketing resources, yet it happens all the time. After all, we pay the VP of Sales a lot of money because of her experience, and we expect that experience will guide her to make the right when things get tough. Though it’s possible that the retail strategy will work, a positive outcome will probably be more about luck than skill.

     
  2. Let’s take a less obvious example of bad use of gut instinct. In this scenario, an app development company is facing a slowdown in the growth of net new business. The VP of Sales wants to make a data-driven decision about why prospects don’t find them as appealing as they used to, and turns to the Sales Operations manager for help. His analysis concludes that the highest value per opportunity for net new is in the mid-size business segment, so the VP of Sales follows his gut instinct and shifts the majority of the net new team’s efforts to focus here.

But when another quarter goes by without improving revenue growth, the need for a solution becomes more acute. All eyes are on the VP of Sales and the net new team. Unfortunately, the analysis failed to take into account the revenue impact of upsell, cross-sell and renewal activity, all of which are critical to assess account LTV, and which would have helped to properly prioritize the investment of sales resources. (For a more detailed analysis of this scenario, check out Think Locally, Act Globally: How the Account Lifecycle Changes Everything).

The problem in both cases wasn’t the use of data, but that the sales leader was using a limited set of data to inform a decision ultimately based on gut instinct.

  • The article may have supported its statements with data, but how likely is it that the data was consistent with security company’s own data?
  • The analysis of the app developer’s mid-size opportunity may have been strong, but its limitation to net new didn’t justify a big shift in resources.

When we have an excess of data about our historical customers and prospects, relying on gut instinct to find meaning in the data is a bad approach. As it turns out, relying on gut instinct isn’t solely a weakness of VPs of Sales. In general, people are very bad statisticians, and we tend to jump to incorrect conclusions based on small sample sizes. To get a deeper understanding of this, read The Undoing Project by Michael Lewis, which explains how belief in the Law of Small Numbers causes us to make inaccurate conclusions based on limited data sets.

The Over-Averaging Problem

A better alternative to gut instinct is to make decisions based on data, specifically large data sets as opposed to small samples. The common perception is that the results of data analysis are more accurate when we have as much data as possible. The hazard of this approach is that we can over-average and produce results that aren’t a true reflection of our business reality.

Again, I’ll share two examples, one involving LTV/CAC and one involving the Ideal Customer Profile (ICP). In both of these scenarios, we recognize the benefit of using all the data available from the customer lifecycle, including data from the marketing automation platform, the CRM, support systems and product usage tools.

Your Business Has More than One LTV/CAC Ratio

Calculating LTV/CAC is a task that often falls to a Chief Financial Officer’s team, which works in collaboration with a business operations (BizOps) leader, whose role may include responsibility for sales operations, marketing operations, business operations and/or financial planning & analysis (FP&A). The collaboration between the CFO’s team and the BizOps leader is critical because determining LTV/CAC requires a complex unwinding of sales, marketing, support and customer success activity data. The data resides in multiple systems including the CRM system, the marketing automation system, the support ticket tool, product usage tracking software, and the company’s finance software.

Calculating LTV/CAC is critical for any business with a recurring revenue model but it’s a complicated effort, particularly when a business has multiple go-to-market channels (e.g., inside sales, outside sales, e-commerce, channel partners, etc.), product lines, industry segments and geographic territories. 

  1. The Upside Problem — If the number is above the generally accepted (though somewhat arbitrary) standard published by David Skok, where LTV/CAC>3, the company will likely assume that all is well and continue to invest in go-to-market activities in all segments, industries and geographies at levels similar to prior years, with the expectation that growth rates will continue as they always have. This is great as long as the company is growing, but as noted in “The Revenue Hit You Didn’t Know You Were Taking: Why LTV-Based Segmentation Matters,” Greg Keshian explains why the company will eventually need to gain a better understanding of the underlying LTV/CAC trajectories that make up the business to guide future performance.
     
  2. The Downside Problem — If the LTV/CAC number is below 3, then the company will likely assume that the business has a fundamental inability to generate the necessary revenue for a given cost structure. They may make black-and-white decisions about the success of a given product line or go-to-market approach and try to shift investment to something that will rescue the business. Worse, the company may seek short-term fixes and change direction repeatedly and frequently, a sequence that can end in a death spiral. If you’ve seen or worked at businesses that made abrupt changes in go-to-market approaches, staff and/or leadership, it’s likely that the downside mistake was in play.

The bottom line: there is no single LTV/CAC profile that defines the health of the business. In fact, every business has a collection of different segments, industries, territories and go-to-market approaches, each with its own LTV/CAC profile. For a business to know where to invest, the BizOps team must be able to calculate LTV/CAC for any segment or industry or territory and slice the results by product line. Importantly, this must be updated on a regular basis, and not just annually, to identify when and if the business should change its investment strategy.

The Fallacy of ICP in B2B

The concept that a business has one Ideal Customer Profile (ICP) makes sense for many B2C businesses, but it fails for many B2B businesses.

The reason is clear when you compare the population of people to the number of businesses in the United States, the world’s largest market. According to the US Census Bureau, there were 323.1 million people in the United States in 2016. By comparison, US Census Bureau data from 2010 revealed that there are only 18,500 businesses with over 500 employees, and only 6 million businesses with one employee or more.

Based on these numbers, it makes sense for there to be a single ICP for B2C businesses because a large enough market can be made from a single set of consumer attributes, such as suburban families with kids and dogs or single urban women between 20 and 40 years old. Big data techniques can be used to roll up the sales and marketing data for B2C businesses and look for the attributes that represent a single ICP.

The relatively smaller number of target businesses in B2B means that few businesses will have enough customers with any single shared set of attributes to build an entire business based on one ICP. In fact, most B2B businesses sell products to multiple segments. Nevertheless, big data techniques have been applied to the B2B world to identify the ICP unicorn. The category of predictive analytics business promises to take all of your customer lifecycle data from all of your customer-facing systems and roll them up into one model of a single ICP. Problem solved, right? Not exactly.

There are several pitfalls of this approach in B2B. By averaging all of the customer lifecycle data together, the ICP loses information about additional attractive segments, each of which can be attractive for different reasons. For example, our cloud-based security software business, sells multiple products into multiple vertical segments.

  • In the retail vertical, Product A has the best win rate for net new business, and Product B is the best for expansion via cross-sell.
  • However, in the financial vertical, Product B has the best win rate for net new business, and Product A is the best for expansion.

This kind of information is lost when rolling up all the data into a single ICP model, and is often invisible in the CRM.

Typical approaches to decision-making may not result in the outcomes we’re looking for.

Typical approaches to decision-making may not result in the outcomes we’re looking for.

Why Zooming In Works

The High-Resolution Business

The reality is that your business is composed of multiple segments, some of which are good and some of which are bad. The ability to zoom in and examine each segment in detail is the key. In the book, Everybody Lies, Seth Stephens-Davidowitz describes this concept as follows:

“This is where the bigness of Big Data really comes into play. You need a lot of pixels in a photo in order to be able to zoom in with clarity on one small portion of it. Similarly, you need a lot of observations in a dataset in order to be able to zoom in with clarity on one small subset of that data.”

For B2B businesses, the bigness of the data comes not from the total number of prospects and customers but rather from the massive amount of activity data that we accumulate about each of those prospects and customers. Because of the power of our marketing automation, CRM, support and usage tracking tools, we have many orders of magnitude of activity data relative to the number of prospect and customer accounts we have. This activity data takes the form of every possible marketing activity (sends, clicks, opens, etc.), sales activity (calls, emails, demos, etc.), support activity (tickets opened, pending and closed, etc.) and customer usage metric (logins, time spent on site, features used, etc.). We know more about what happens during the account lifecycle than ever before, but we’re not taking full advantage of its power in the aggregate. We’re not zooming in.

In order to make this data usable, we need to make it visible by pulling all of the data relating to a given account from each system into a single master account object. Further, to examine how this master account object behaves over time, we must be able to see changes in activities in that account over time. Once we have the master account object, then we have enough pixels, or data in this case, to zoom in and understand our business at the account level — or at any possible roll-up of our accounts, whether at the segment level or for the business overall.

The ability to zoom in on our accounts means that we can, for the first time, distinguish between good accounts and bad accounts in terms of their ability to grow revenue over time. When confronted by slowing growth, we no longer need to rely on our gut or on averages. Now we have the power to identify the attributes of the accounts and segments with the best potential revenue growth over time and then orient our marketing and sales activity to find look-alikes. Stephens-Davidowitz describes this as the search for doppelgangers:

“A doppelganger search … zooms in on the small subset of people most similar to a given person. And, as with all zooming in, it gets better (with) the more data you have.”

Equally important is the ability to find accounts and segments that perform less well in building revenue over time. These accounts and segments are like a cancer on our business because they limit our overall growth. The difficult part is investing sufficient analysis effort to identify accounts and segments you’re actually no longer going to pursue. It can be counterintuitive: if a particular segment performs very well in terms of net new business but very poorly in terms of expansion over time, we need very solid data in order to convince the sales team to say no to those accounts. When teams operate in organizational and data silos, it can even be tough to get them to come to the meeting. The data must be definitive to support what is for many a very tough call.

Emergence of the Sales Scientist

In Winning Parts 1 & 2, we wrote about the emergence of the strategic BizOps leader’s role in growing B2B recurring revenue businesses. Based on my experience, BizOps is a core competency in B2B sales and marketing with four critical capabilities:

  1. The ability to pull together large datasets captured throughout the entire account lifecycle — typically from disparate systems — in order to zoom in on a master account object;
  2. The capacity to slice and dice master account objects by geography, product, and more, in order to understand which accounts and segments are best for growing revenue over time;
  3. The skill to align the go-to-market team around the best accounts and segments by sharing the results of a complex multi-object data analysis in a simple, clear and convincing format that everyone can understand; and
  4. The power to operationalize the results of this analysis by pushing prioritized lists of accounts to the sales team to enable them to focus only on the accounts that drive the most revenue over time.

Like sales scientists, BizOps leaders need the ability to run experiments about who the best accounts are, and test theories about whether a certain segment will generate growth without putting the entire business at risk. For example, in the case of our cloud-based security software business, there were significant ramifications from the discovery that the mid-sized business segment was not attractive in terms of LTV. Going after a different segment can require an adjustment of the entire sales process and sometimes the entire team, with ripple effects in marketing and support as well. In short, the decision should not be taken lightly. Too often, the pressure to grow produces a decision to change everything overnight, resulting in a major disruption to the business and to the people involved.

A better data-driven approach is to run an experiment to test the hypothesis first. If the hypothesis says that the business should try a higher touch sales approach with larger customers, then a small portion of the team can be separated out and provided with a filtered list of target accounts. Marketing activities can be coordinated to generate awareness and leads at just these accounts. With master account objects in place, the BizOps and sales teams will be in position to study the results and then make a strong case to transition the business in a way that maximizes return over time and minimizes the negative disruption to the business and its team.

Stephens-Davidowitz described the power of experimentation as follows:

“[Experimentation]...makes randomized experiments, which can find truly causal effects, much, much easier to conduct — anytime, more or less anywhere, as long as you’re online. In the era of Big Data, all the world’s a lab.”

It takes work to make this happen in B2B, but the benefit of getting it right can mean the difference between success and failure.

Account Lifecycle Management: The Powerful New Data-Driven Approach for B2B Sales

Years ago, when it wasn’t so easy to record and analyze sales activity data, we relied on the expertise of sales rock stars to help shape and execute go-to-market strategies to drive toward company goals. Today, when even the smallest company has access to powerful marketing, sales and customer success software, we have more data than we know what to do with. And despite the availability of this data, our teams stay in their siloed comfort zones, with each optimizing its performance toward department-level activity-based KPIs, not necessarily company-wide growth. Why hasn’t more data resulted in better decisions, more growth, and more predictability?

This is the promise of account lifecycle management: understanding accounts at the data level leads to new levels of understanding, which go beyond reporting to strategic decisioning, and ultimately driving action across the business to maximize recurring revenue.
 

Account Lifecycle Management presents a new opportunity to view all account data holistically. This informs our understanding of the best opportunities to support cross-functional alignment and drive effective action.

Account Lifecycle Management presents a new opportunity to view all account data holistically. This informs our understanding of the best opportunities to support cross-functional alignment and drive effective action.

The Transformation of B2B Sales Software

Since 1993, when Tom Siebel left Oracle to form Siebel Systems, the world of B2B sales has been defined by the way we use software to acquire and analyze sales data. Though the CRM still dominates B2B sales, we believe that the rise of the $36 billion CRM industry represents merely the first era of B2B Sales Software, and that we are entering a new era where the CRM is no longer sufficient to drive the complex decisions required to build business value.

In the first era, the CRM took hold and has become dominant. Products like Siebel and later Salesforce showed that it was possible to take a snapshot of your current set of sales activities to inform what was happening in the business. In the first era, we:

  • Built our sales processes in order to drive the volume of sales activities and measured conversion rates in a constant effort to improve the efficiency of closing deals;
  • Adapted to workflows, procedures and even sales process vocabulary that reflected the vendor-centric way of managing the sales team;
  • Relied on the tactical BizOps team to support and maintain our data, processes and systems; and
  • Consumed data to drive reporting about how individuals, teams and the overall sales organization had been performing at a given point in time.
Recurring revenue businesses today can leverage account lifecycle data to move from process-driven reporting to data-driven decisioning.

Recurring revenue businesses today can leverage account lifecycle data to move from process-driven reporting to data-driven decisioning.

In the emerging second era, which I like to refer to as the BizOps era, we’re seeing massive transformation. The emergence of hundreds of new products to automate sales, along with similar shifts in marketing automation and customer service, coupled with changing ways that customers buy, have set the stage for new ways for sales leaders to use account lifecycle data to drive the business forward. In this 2nd era, we:

  • Realize that enterprise value depends on our ability to improve the effectiveness of our sales teams in order to drive recurring revenue and increase the lifetime of our accounts;
     
  • Need to break down the silos of tools and teams that characterize the traditional, linear marketing funnels and sales pipelines and adopt a customer-centric mindset and a lifecycle perspective;
     
  • Depend on an increasingly strategic BizOps team to gather and analyze volumes of data from go-to-market efforts across the company in order to align our teams toward shared goals; and
     
  • See data as something more powerful than just numbers on a report, leveraging it as a decisioning tool that drives our investments in sales and marketing resources to achieve our business goals.

By embracing the account lifecycle management methodology, BizOps teams at B2B companies can leverage the power of customer account data to shine a bright light on the best accounts and the best opportunities for creating and sustaining real revenue growth. For the first time, they can use the myriad advantages of account lifecycle management to deliver real value and effectiveness of sales and marketing resources, support a customer-centric approach in all aspects of selling and marketing, partner with strategic BizOps colleagues to improve go-to-market strategy and execution, and make more informed decisions when charting the path forward.

What’s Next?

Here are some things that you can do right now to explore and leverage account lifecycle management:

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Alex Laats

Alex is CEO and Founder of Rekener. Previously, he served as President and COO at ZeroTurnaround and as President of the Delta Division of BBN Technologies. At ZeroTurnaround, he grew high velocity inside sales by 6x in 3 years. At BBN, Alex co-founded RAMP and AVOKE, both recurring SaaS businesses based on BBN's world class speech recognition and natural language processing tech. Alex started his entrepreneurial career as founder and COO of NBX Corporation, which led the transformation of business telephone systems to Voice over IP. Alex’s companies have generated $500M in liquidity events and more than $1B in sales.

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