Emissary Launched

Emissary Sales Coach Profile
Emissary Sales Coach Profile

Emissary, a novel concept around account-specific sales coaching from former employees of key accounts, was formally launched this month.  The new service, which recently received $10 million in Series A funding, pairs up former enterprise executives, or “emissaries,” with sales reps to provide account guidance.  The initial set of emissaries focuses on two verticals: Enterprise Software and Marketing & Advertising.

According to the firm, “By directly connecting clients to former executives who have accumulated invaluable knowledge throughout their careers, Emissary takes over where Google searches, social networks and sales automation software leave off. Over 5,000 experienced business leaders on Emissary provide personalized insights about the organizations they have previously worked – such as what the company culture is, who the key decision makers are and how the company makes buying decisions.”

Emissaries are vetted by the firm to ensure they have the requisite knowledge and experience to guide enterprise sales reps.  The firm’s Salesforce synch connector matches sales organizations that “demand their insight” with emissaries holding “tacit knowledge” of organizations.  The platform then facilitates communications, much of which is e-mail.

Emissary views itself as a sales acceleration platform, but one that focuses on closing deals instead of generating more leads.  Thus, emissaries assist with much of the account intelligence which doesn’t reside online, helping reps understand organizational culture, procurement processes, and key decision makers.  This tacit knowledge is often lacking online.  Hammer, a former Google Product Executive, noted that even heavily data-driven organizations such as Google often make mistakes because “Often times, we didn’t have access to a piece of knowledge that sat in someone else’s head, and we didn’t know who that person was. I created Emissary because I believed if we faced that problem at Google, that organizations of all sizes must be facing that challenge.”

“At Google I came to realize that we all have valuable, tacit knowledge that was not available online,” said CEO and Founder David Hammer. “With Emissary, we’re using technology to gain access to relationship-driven knowledge from trusted sources that can often make the difference between whether or not you close a deal.”

The Emissary service requires a significant upfront commitment “in the tens of thousands of dollars, with the price depending on each client’s specific needs.”  Contracts run six to twelve months and include a set of Emissary engagements.

Emissary recently closed on a $10 million Series A led by Canaan Partners and G20 Ventures.  The Manhattan-based firm previously received a $2 million seed round from The New York Times, Google Ventures and Nextview Ventures.

With AI, It is Garbage In, Garbage Out

scrapyard-70908_960_720

A few weeks ago, Salesforce announced its new Artificial Intelligence (AI) functionality called Einstein.  The new features promise to provide improved decision making based upon predictive scores and recommendations to sales, marketing, service, and other functions.  Likewise, Microsoft announced yesterday that they have formed a dedicated AI group working on infusing Microsoft products with intelligent capabilities.

However, as AI and Predictive Analytics become key technologies for companies, it is important to remember the old GIGO maxim:

Garbage In, Garbage Out

These tools simply won’t work well if your information is inaccurate, out of date, or incomplete.  Best case, bad data results in weak predictions that aren’t trusted.  Worst case, they provide a false confidence that wastes resources and misdirects corporate activities.

John Bruno, an analyst at Forrester, described this problem well in a recent blog:

The future analytics-driven sales processes is bright, but the path ahead is not without its challenges. Current and potential Salesforce customers should be mindful that intelligent recommendations require a large volume of quality data. If poor data goes in, poor recommendations will come out. Cleansing data and iterating the fine-tuning of recommendations will be vital to long-term success.  Another major hurdle is adoption. Many sellers still lack trust in “intelligent” recommendations. You will need to handhold these sellers until they form trust. This means starting with small recommendations and scaling from there.

The good news is that many of the sales intelligence companies are now offering data hygiene services for lead, contact, and account records.  The processing can be performed via CRM or MAP connectors or by uploading files to their cloud services.  The vendors match sales and marketing files against their reference datasets and then augment the files with firmographics, biographics, technographics, etc.  Matching can be done both in real-time to support both list uploads and web forms and via batch processing to support on going maintenance of corporate data.

While no company and contact database is 100% accurate, they are far more accurate than most marketing automation platforms and CRMs.  Furthermore, they have better field fill rates, standardized values (important for segmentation and analytics), and more rapid update cycles.

The predictive analytics companies are also beginning to provide enrichment services.

 

 

SalesLoft: Excellence in Product Release Announcements

sl-product-releaseAs part of the update to my Field Guide to Sales Intelligence Vendors, I’m working my way through the product updates of nearly two dozen vendors.  In most cases, this information is only available to non-users as press releases and blogs.  But one of my profiled companies, SalesLoft, has a better way to communicate to its customers and prospects.  Their VP of Product Strategy Sean Kester records a ninety second video for each release which includes a mini-demo and user benefits discussion.

These videos are short and professional with optional closed captioning and a full transcript.  Furthermore, the videos are in plain English so that they can be easily understood by end users.  You won’t hear the typical tech speak.

And videos that start, “Hey, Guys, Sean here with SalesLoft” help to reinforce the brand and convey a sense of friendliness and ease of use.

Here is a sample transcript:

Hey Guys, Sean here with SalesLoft. We are excited to announce the new SalesLoft platform UI.

Today we’re introducing a new look. You’ll see an updated platform design for SalesLoft. Inspired by the desire to deliver consistency, the new UI represents a simpler interface, standardizing all the pages within the platform.

We made changes to how users interact with the interface:

The simpler design puts more focus on the information that matters without changing how you navigate throughout the platform…

The SalesLoft community has evolved over the past few years, becoming the application of record for the specialized inside sales organization.

The updated UI reflects your feedback, making it easier to quickly execute your tasks in addition to laying a foundation for faster innovation and increased value delivery to you, our users.

Thank you for enabling SalesLoft to continue to serve you. As always, we are excited to hear your feedback!

What’s more, it is clear that they understand that meeting the needs of their customers drives their success.

Bravo!

Sales and Marketing Alignment

In a research study titled, “2016 B2B Sales & Marketing Collaboration Study,” Samantha Stone and The Marketing Advisory Network found that the misalignment of sales and marketing objectives remains a key problem for B2B companies.  Although this has been a topic of discussion for several years now, misalignment remains a key stumbling block to meeting revenue objectives.  Finger pointing between sales and marketing has long been a blogging meme.

When asked about whether marketing co-workers were doing a “superb job of supporting sales efforts,” sixty percent of marketers agreed while only twenty percent of sales executives agreed.

Other signs of disconnection between the two parties:

  • Only twenty percent of marketers believe that there is a 95% follow up on marketing generated leads while only half of sales executives believe a 95% follow up rate is maintained within their department.  Overall, 57% of respondents believe that no more than 85% of marketing leads are acted upon by sales.
  • Marketers have little confidence that sales reps are using the tools they develop for sales.  While only 15% of marketers believe their tools are broadly adopted (“virtually 100%”), over half of sales reps believe the tools are being fully deployed.
  • While fewer than twenty percent of marketers believe that sales is rewarded for supporting marketing objectives, 55% of sales teams believe their rewards are aligned with marketing.
  • Firms that did not share key performance indicators between sales and marketing were half as likely to exceed revenue targets.
  • Marketing ownership of pipeline acceleration is critical to meeting revenue targets.  “Organizations that exceeded revenue goals in the last 12 months are 3X as likely as those that miss revenue goals for marketing to “own” pipeline acceleration (not just lead generation),” said Stone.

“It’s common sense that organizations that rally together around shared goals will drive more efficiency than those where different functions are at odds with each other. Yet, most sales and marketing teams struggle with achieving this ideal.  That’s almost terrifying given we know fully integrated companies are more profitable, drive faster growth and make happier customers,” said Stone.  “Sales and marketing leaders are smart, yet almost every organization I walk into has some level of unhealthy tension between the two groups. It doesn’t seem to matter the size of the company, the industry they serve or how fast they are growing. In fact, it’s so common we accept it as inevitable.”

Sales and Marketing SLAs (Source: Marketing Advisory Network)
Sales and Marketing SLAs (Source: Marketing Advisory Network)

The study also found that setting Service Level Agreements (SLAs) between sales and marketing are highly correlated with revenue performance.  Firms that took simple steps such as defining lead scoring criteria and lead follow up timeframes were much more likely to exceed goals than fail to do so.  Of the six SLA goal categories defined by Stone, five were associated with “exceeded revenue goals” more commonly than missing revenue goals by more than ten percent.

The one goal that was not associated with outperformance, agreeing on the “number of new contacts added to the database by sales,” doesn’t address the mix of marketing and sales generated leads.  If we assume that there is an optimal percentage of sales generated leads, then agreeing on a percentage that is significantly above or below the target would be sub-optimal.  Absent a way to determine this optimal mix, setting an SLA on sales generated contacts could easily result in too much or too little time spent on contact identification.  If the number is too high, then reps are likely to add contacts of little value to the CRM so as to reach numeric targets.  A smart SLA would be based upon an analysis of the cost of generating sales contact records against the benefit of adding additional contacts.  As such, setting an analytics-free numeric target is no better than having no SLA at all and allowing each rep to determine their optimal contact discovery level.

Teams that perform best, document more service level agreements between sales and marketing than those teams that simply meet or miss revenue goals,” said Stone.  “Those that exceed revenue goals even collect data points such as win/loss data in a formalized manner.  Perhaps the most simple practice they follow is not only agreeing on lead scoring critical for sales follow up, but on time from lead assignment to follow up. It’s this closed loop accountability that clearly makes a difference.”

Finally, one simple step for improving revenue is for marketing to attend sales meetings.  The study found that B2B organizations which outperform on revenue are twice as likely to have marketers attend customer and prospect meetings than firms that fail to meet revenue targets.  Furthermore, marketing departments should be surveying the sales team on tools.  Stone found that firms “that exceed revenue goals are 3.1X as likely as those that just meet revenue goals to survey buyers when evaluating sales tools and 14X as likely as those that miss revenue goals.”

Tibor Shanto, Principal of Renbor Sales Solutions, calls for improved cooperation between sales and marketing under the leadership of a Chief Revenue Officer:

I have always seen sales and marketing as being on a shared mission and fighting the same battle. Like the military, to succeed, marketing has to provide air cover for the ground troops, namely sales, and this requires complete coordination, planning, execution, and review. This needs to extend from lead generation through all stages of the sale. At each stage, marketing offers up different coverage based on the feedback from sales. And sales needs to be sure to provide that feedback every step of the way or the air cover may miss the mark. While each branch of the military has their command, the overall effort is led by the commander. That’s why I am a fan of companies having a Chief Revenue Officer, rather than a distinct VP Marketing and VP Sales.

It is through a recognition of shared goals subject to shared metrics and feedback loops that firms can obtain improved performance from the two departments that own revenue generation.

The research was sponsored by QuotaFactory.

Still Not Convinced that Data Quality Is an Issue?

 

Integrate evaluated over 750,000 records from B2B companies and found consistent data problems whether the firm was and SMB, Enterprise, or Media Company. In each case, roughly 4 in 10 records contained inaccurate or bad information.
Integrate evaluated over 750,000 records from B2B companies and found consistent data problems whether the firm was an SMB, Enterprise, or Media Company. In each case, roughly four in ten records contained inaccurate or bad information.

While the primary theme of my blog is sales intelligence, you cannot have sales intelligence if your databases are rife with duplicate records, invalid emails, missing or incorrect firmographics, and non-standardized values.  These errors wreak havoc on marketing and sales.  I came across a 2016 Integrate report that had a series of quotes on the subject which addressed the impact of bad data quality across multiple marketing activities:

The issue of data quality continues to be one of the biggest roadblocks to effectively analyzing the prospect and customer journey. It also dramatically increases the costs of analytics projects and negatively impacts performance.
– Sameer Khan, Sr. Product Marketing Manager, IBM Customer Analytics


Dirty data is the silent killer of marketing campaigns. It makes you look bad, depresses the impact of great content and offers, and can put your brand, reputation and domain at risk (or worse).
– Matt Heinz, President, Heinz Marketing


Data is the oil of any marketing engine, and in order to create perpetual demand generation, data accuracy needs to be a top priority. Marketers must be ruthless and deliberate about data quality and standardization at point of entry.
Jonathan Burg, Sr. Director, Marketing+Customer Acquisition, Apperian


And if you still aren’t convinced that data quality can choke your initiatives, their research found that 40% of B2B records have some form of data quality issue with duplicate data representing 15% of your marketing database.  Invalid Values and Ranges (10%) and Missing Fields (8%) were also common problems.

So if forty percent of your marketing data is faulty, then a significant percentage of your Marketing Qualified Leads passed to sales will contain errors including contacts not at companies (not evaluated by Integrate but contacts decay at a 25% rate per annum), and bad firmographics resulting in wasted time, incorrect routing,  inaccurate lead qualification, and poor messaging.  Furthermore, initiatives such as account based marketing (ABM), account based sales development (ABSD), and predictive analytics will stall if they are fueled by bad data.

Owler: Jim Fowler on Crowdsourcing Content

Owler Profile of Lyft

Jim Fowler, who founded three crowdsourcing startups (Jigsaw which was acquired by Salesforce.com and renamed Data.com,  InfoArmy, and ), was asked how crowdsourcing has changed over the past decade.  His observation was broader than crowdsourcing and applied to any tech company looking to gain mindshare:

I think they change in the same way that we all have. We all are just overloaded with information.  Getting people’s time and getting them to pay attention is much more difficult now than it was back in the beginning of Jigsaw for sure. Getting journalists and analysts to talk and write about you is different because there’s so much going on. In fact a lot of the big publications don’t even exist or don’t write about it anymore.

It’s become much more flat, if you will. More players in it, so that’s interesting, but I just think the biggest thing is just people … There’s so much stuff flying around out there now that really making sure you have a crisp clear message so that they understand the value is even more important than it ever was and that’s just been the big change. People are more sophisticated, they’re more … They know how to use data and I see that trend continuing.

Fowler also noted that Owler combines crowdsourcing and semantic mining with editors.  While machines can do much of the work around event aggregation and structured alerts for exec changes, M&A, and funding rounds, editors ensure that information is properly tagged and mapped.  While this editorial review of news introduces a short delay in information delivery, it reduces the number of false positives and passing mentions of companies.  Furthermore, it allows them to de-dupe the stories and accurately capture M&A and funding content.

Basically, it solves your signal to noise problem through the addition of a short editorial review step.

If you just used technology to try to do this, you would get a lot of noise in there because really it’s a lot harder than it looks to figure out that the article is actually about Apple. Apple gets mentioned in millions of articles. To know that it’s actually about Apple is … To just do it with technology is really hard. What technology can do is say, “We think this is an article about Apple and we think it’s an Apple acquisition and we think this is the company that they did and we think this is it,” but what you need to do is create a task that gets prioritized very highly that a human looks at really quick. Checks out all the data and goes, “Ah, that’s right. We’re good,” and then sends it on to the people.

Otherwise you get a lot of noise, what I’m getting at is that technology can get you way down the road, but you need humans to get you all the way down the road if you want high quality data.

It is this multi-process approach that is likely to be the future of data collection and aggregation.  Traditional methods of data collection via phone interviews or analyzing filings information are quite expensive while semantic mining can get tripped up on context (is this about company X? Is this a relevant story? Is this a discussion of current events? Is this an actual event, proposed event, or mere rumor?).  Likewise, crowdsourcing requires a very large audience to obtain the wisdom of the crowd and works best on easily defined fields such as address, phone, and email (i.e. Jigsaw contacts).  Crowdsourcing also works well at gauging sentiment.  For example, Owler captures sentiment around whether the CEO is doing a good job and the projected fate of private companies.  But crowdsourcing does a poor job around complex information such as industry code tagging or corporate linkage.  It is through complementary methods that vendors will drive qualify forward while keeping data costs in check.

GIGO: Did We Lose on Price Again?

Loss Reason

Steve Silver, a Research Assistant at Sirius Decisions, recently blogged about a client where the overwhelming reason for losing deals was price.  But the client had a differentiated service where price should not have been the primary factor.

Silver discovered the reasons for this anomaly:  The field was not used by any departments at the firm.  Without an owner, the path of least resistance was selected — the first choice in the picklist.  And in the case of the client, 90% of the losses were flagged as price-based.

Did we establish value?

Silver omitted a third reason, and one which is common amongst sales reps.  Price is an easy scapegoat for lost opportunities.  But if your service is well differentiated and you focus on your value proposition, price should not be the primary loss driver.  Yes, some deals will be lost because a competitor low balls the deal (a true price loss), or the prospect simply does not have the financial means to purchase your service (a poorly qualified prospect), but in most cases, losing on price is a failure on the part of sales reps.  If they thought about it more, they would realize that price is not an exogenous variable outside of their control.  That’s because price is tied to value.  Price is the critical variable if your value has not been established.

This isn’t to say that pricing could be wrong.  If your competitors are quickly moving up the value curve, your historical price may no longer be sustainable as you become less well differentiated.  With good data and analytics, you would capture this shift in the competitive marketplace and act accordingly (e.g. R&D to better differentiate your service, better product bundling, or reduced prices), but price should only dominate the loss reasons in a commodity business.

GIGO

So what else could be gleaned from this situation?  First, somebody needs to own data quality within the CRM.  If a field is viewed as busywork, your sales reps will populate it with junk data.

Garbage in, Garbage out.

Managers should also be pushing back on reps to better understand why deals were lost so that mistakes can be avoided in the future.  Does the sales rep need additional training or coaching?  Are additional sales tools needed for competitor handling or establishing value?  Are we poorly qualifying opportunities or failing to identify the key decision makers?

Yes, it is easier to move onto the next deal without taking the time to analyze deal losses; but a learning organization needs to understand its failure points.

Sales Operations

Sales Operations should be cross-checking fields.  If the loss reason is price or features, then a competitor had a better offering.  Was the primary competitor recorded in the CRM?  If the competitor is blank, then additional explanation should be required.  Did you really lose on price or features if you don’t know who the competitor was?

Or did you lose to no decision or the incumbent because there was insufficient value established to warrant funding the purchase or sustaining the switching costs?

If you don’t collect the data or you allow a field to be treated as busywork, it won’t be available for analysis.  I have had several instances where my clients did not record the loss reason or the competitors.  I have also had others where the fields were usually blank.  In short, the firms were operating in a competitive fog and not using their CRM for market monitoring.

In the end, it is important to not only gather win/loss information, but to use the data for sales training and coaching, marketing communications, sales enablement, and product development.  When information is valued by the organization, then sales reps are less likely to blithely skip fields or enter the first field in the required picklist.