Selling

What are you Really Selling? (prospecting tips included)

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It is interesting to see people in Sales and Marketing still focusing on features, performance, cost, and even value without creating linkage to what that means to a company from a business perspective. Once you understand what you are really selling, which means why people really buy what you are selling, it is possible to connect with prospects in a meaningful way that can increase your win rate.

Pot of Gold

There is a sales adage from the 1940s (source) that asserts, “No one wants a drill. What they want is the hole.” That basic understanding of why people and companies buy is still often lost in sales and marketing messages today.

Several years ago my team and I were selling a new Analytics Database that was truly different, but our message was identical to every other database vendor – “70% – 100% faster than every other product.” It is nearly impossible to differentiate your product with a non-differentiated message.

I flipped the messaging to focus on business needs. We created a weekly webinar focused on Why Fast Matters. Query response time is important, but being responsive to business customer requests is often more important. What if they did not need to wait a week or two to have new indexes created or a month to have a Star Schema updated? They could just run queries as-is, maybe wait a minute instead of a second or two, and have what they need then and there. That message resonated and we sold the first 50% of that product globally. When the Australian team began using our messaging their sales also increased. Funny how that works.

Effectiveness is all about results, and efficiency is all about achieving those results with the least amount of time and effort. This doesn’t mean that we are looking for a lazy approach to find a win. Rather, it is about identifying repeatable patterns of doing the right things that circumvent unnecessary activities, time spent, and associated costs. Being good at qualification doesn’t mean that you will be good at closing, but it is tough to become a good closer without having sufficient “at-bats” that good qualification leads to.

The way to help yourself understand what you are selling is to view things from your prospect’s perspective. What struggles are they likely facing? Where are the greatest opportunities to help their type of business? Are you analyzing data to attempt to assess their unmet needs? Your insight can become a huge differentiator, especially if you can teach them different and better ways to do something (ala the Challenger Sales Model).

What is the difference between your prospect company and its main competition? This analysis requires a general understanding of their vertical and more specific understanding of the prospect company, it’s history, and 2-3 of their main competitors. It also requires an honest understanding of how your company and products compare to the competition so that you can play-up your strengths and limit your investment in areas where you are less likely to win the business.

Now that you have identified an area where you believe there is a good fit the next step is to develop your target list for that profile. Much of the information you need can be found in Corporate filings (10-K and 10-Q filings for public companies, and Form 5500 filings for companies with a 401(k) plan – especially useful for private companies), websites like Owler.com and SimilarSiteSearch.com, and from social media sites like LinkedIn.com and Facebook.com). Then search for people in areas that are most likely affected and look for titles that are likely Stakeholders or Decision Makers.

The next item to focus on is messaging. Below are a few examples from my career –

  1. Analytics & Big Data – The focus here is often on data volume, the currency of the data, speed of queries, cost, maintenance, and downtime. Those things become important later in the sales discussion, but initially, companies want to know what problems your product or solution will solve.
    • Some of my fastest deals sold because I demonstrated ways to make better decisions faster and/or identify problems before they were had the chance to become major problems. Avoiding problems and unplanned outages were key parts of the messaging.
    • In one case I was able to close a significant deal in less than three months by focusing on how a company could provide five years of transactional data for their customers to use to make purchasing decisions in less time than it took the current system to analyze six months of data. Their sales increased after implementing the revised system. Helping their customers make better buying decisions faster was the winning message.
  2. Embedded Products – While many companies focus on APIs, features, or cost per unit, I would focus on how the product I was selling made things better and easier for Customer Support and Customer Satisfaction. Things like stability, lack of maintenance required, data integrity, performance over time, messaging when something abnormal or concerning was observed, etc.
    • I sold a $1.1 million deal in less than two months to a medical device company by focusing on the life of those devices often being 10-15 years and how their customers need to be assured that the results will be the same from machine-to-machine, even if one of those machines is much newer than the rest of the machines. Consistency over time was the winning message here.
    • After being approached by a Defense Contractor for a relational database product for a new Flight Simulator system I changed the discussion to the complexity of flight control systems, the need to correlate 30+ operational systems in real-time, and the importance of taking a verbal command and translating it to specific commands for each system. That led to the sale of a NoSQL product that was ideally suited for this complex environment. The idea of letting our software handle the really complex work helped win this deal.
  3. Consulting Services – This is not contracting or body shop services (commodities), but true Business and Technical Consulting services that were high visibility and high impact. In these cases expertise, experience, and having a track record of success in different but demanding scenarios provided confidence. Often these were multi-phase engagements to first prove our value before making a large commitment.
    • In a bid against two well-established competitors, we won a deal with a large Petroleum company that was nearly $500K. The proposal included information that we uncovered about the system and use case and later verified with the prospect, a section on our people and some past projects, and then a high-level project plan with firm-fixed pricing. We won the bid and I later found out that our cost was $50K higher than the largest competitor and more than $100K more than the other competitor. The customer told me that, “Your proposal demonstrated the understanding of who we are and what we need, and that confidence provided the justification to select your company and pay a premium to have the job done right the first time.”
    • My first million-dollar deal was with a company that we demonstrated our ability to solve problems. They knew they needed assistance but were not exactly sure where. I created a “Pool of Days” concept that provided flexibility in the work performed (task, deliverables, and scheduling) but had minimum monthly burn rates and an expiration date to protect my company. This led to many other deals of this nature with other companies. Flexibility and the ability to accommodate changing needs without introducing significant risk or additional cost was the winning messaging here.

As you see from these examples the common theme is helping companies solve their specific business problems. Even in cases where technology was central to that message the focus was always on better results for that prospect and their customers. Value is important but the results matter even more for most purchasing decisions.

Nobody wants to be responsible for taking a chance on a new vendor and be responsible for a high-profile failure. Helping instill confidence early on makes a huge difference and following-through to successful implementation results in happy customers who become great customers and provide important referrals.

It all starts by selling what you know you can do from a business perspective for your Prospects needs to make their lives easier and business better, rather than selling what you know you have from a technical perspective where you are far more likely to get lost in the noise of the messaging from most of your competition.

Shouldn’t Sales Forecasting be Easy? What about Accuracy?

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I’m sure that everyone has read articles that state some “facts” for managing your “sales pipeline” or “sales funnel.” Things like needing 10x-30x of your goal at the start of the process, down to needing 2x-3x coverage at the start of a quarter to help increase your odds of achieving your goal. Now, if it was only that easy…

First, what are you measuring? The answer to this question is something that anyone with a sales quota should be able to succinctly answer. For example, are you measuring?

  • Bookings – Finalized Sales Orders
    • What happens when Sales Operations, Finance or Legal pushback on a deal? You have a PO, but has the deal really been closed?
  • Billings – Invoicing Completed
    • This includes dependencies that have the potential to introduce delays that may be unexpected and/or outside of your control.
  • Revenue – In-depth understanding of Revenue Recognition rules is key.
    • How much revenue is recognized and when it is recognized varies based on a variety of factors, such as:
      • Is revenue Accrued or Deferred? This is especially key for multi-year prepaid deals.
      • Is revenue recognized all at once – such as for the sale of Perpetual Software Licenses? (even this is not always black and white)
      • Is revenue recognized over time – such as with annual subscriptions that are ratable on a monthly basis?
      • Is revenue based on work completed / percentage of completion? This is more common with Services and Construction. Combining contracts, such as selling custom consulting services with a new product license, can complicate this.
      • Are there clauses in a non-standard agreement that will negatively affect revenue recognition? This is an area where your Legal team becomes an invaluable contributor to your success.
    • Cash Flow – Is this really Sales forecasting?
      • The answer is ‘no’ in terms of Accounting rules and guidance.
      • But, if you have a start-up or small business this can be key to “keeping the lights on,” in which case the types of deals and their structure will be biased towards cash flow enhancement and/or goals.

 

My advice is to work closely with your CFO, Finance Team, Sales Operations Team, and Legal team to understand the goals and guidelines, and then take that one step further to create policies that are approved by those stakeholders and are then shared with the Sales team to avoid any ambiguity around process and expectations.

So, now the hard part is over, right?

Diagram showing upward trend over the word Sales.It could be that easy if you only have one product that is well established, has a stable install base, has no real competitive threats, where the rate of growth or decline is on a steady and predictable path, and where pricing and average deal size is consistent. I have not seen a business like that yet but would have to believe that at least a few of them exist.

Next, what are you building into your model to maximize accuracy? Every product or service offered may be driven by independent factors, so a flat model that evenly distributes sales over time (monthly or quarterly) is just begging to be inaccurate. For example:

  • One product line that sells perpetual licenses may be dependent on release cycles ever 18-36 months.
  • A second product line may be driven mainly by renewals and expansion on fairly stable timelines and billings.
  • A third product line may be new with no track record and in a competitive space – meaning that even the best projections will be speculative.
  • And finally, there could be Services associated with each of those product lines and driven by an even greater number of dependent and independent factors (new implementations, upgrades, implementing new features, platform changes and modernization, routine engagements, training, etc.)

 

Historical trends are one important factor to consider, especially because they tend to be the thing that you have the greatest control over. This starts with high-level sales conversion rates and goes down to average sales cycle, seasonal trends, organic growth rates, churn rates, and more. Having accurate data over time that can be accurately correlated is extremely helpful. But, factors such as Product SKU changes, licensing model changes, new product bundles, etc. increase the complexity of that effort and potentially decrease the accuracy of your results.

Correlating those trends to external factors, such as overall growth of the market, relative growth of competitors, economic indicators, corporate indicators (profits, earns per share, distributions, various ratios, ratings, etc.), commodity and futures prices (especially if you install base tends to skew towards something like the Petroleum Industry), specific events, and so forth can be a great sanity check.

The best case is that those correlations increase your forecasting accuracy for the entire year. In all likelihood what they really do is provide valuable inputs that allow you to dynamically adjust sales plans as needed to ensure overall success. But, making those changes should not be done in a vacuum, and communicating the potential need for changes like that should be done at the earliest point where you have a fair degree of confidence that change is needed.

There will always be unexpected events that negatively impact your plans. Changes to staffing or the competitive landscape, reputational changes, economic changes, etc. can all occur quickly and with “little notice.” That is especially true if you are not actively looking for those subtle indicators (leading and trailing) and nuances that place a spotlight potential problems and give you time to do as much as possible to proactively address them. Be prepared and have a contingency plan!

Forecasting accuracy drives confidence, and that confidence leads to having the ability to do things like getting funding for new campaigns or initiatives. Surprises, even positive ones, are generally disliked simply because the results were different than the expectations and that begins to fuel other doubts and concerns.

Confidence comes from understanding, good planning, helping everyone with a quota and the teams supporting them to do what is needed when it is needed to optimize the process, and then to have an effective approach to determine whether deals really are on-track or not so that you can provide guidance and assistance before it is too late.

It may not be easy, but it is the thing that helps drive companies to that next level on a sustainable growth trajectory. In the end, that is what matters the most to the stakeholders of any business.

 

As an aside, there are myriad of rules, regulations, and guidance statements provided by a variety of sources that apply to each business scenario. I am neither an Accountant nor an Attorney, so be sure to consult with the appropriate people within your organization or industry as part of your routine due diligence.

Good Article on Why AI Projects Fail

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high angle photo of robot
Photo by Alex Knight on Pexels.com

Today I ran across this article that was very good as it focused on lessons learned, which potentially helps everyone interested in these topics. It contained a good mix of problems at a non-technical level.

Below is the link to the article, as well as commentary on the Top 3 items listed from my perspective.

https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html

Item #1: 

The article starts by discussing how the “problem” being evaluated was misstated using technical terms. It led me to believe that at least some of these efforts are conducted “in a vacuum.” That was a surprise given the cost and strategic importance of getting these early-adopter AI projects right.

In Sales and Marketing you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither initially vet the solution nor quickly qualify the need for your solution when speaking with those customers or prospects. That leaves a lot of room for error when transitioning from strategy to execution.

Increased collaboration with Business would likely have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. To me, this should have been called out early as a major lesson learned.

Item #2: 

This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.

That was interesting because it appears that assumptions may have played a part in overlooking other aspects of the problem, or that the teams may have been overly confident about obtaining the correct results using the data available. In the examples cited those teams did figure those problems out and took corrective action. A follow-on article describing the process used to make their root cause determination in each case would be very interesting.

As an aside, from my perspective, this is why Explainable AI is so important. There are times that you just don’t know what you don’t know (the unknown unknowns). Being able to understand why and on what the AI is basing its decisions should help with providing better quality curated data up-front, as well as being able to identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.

Item #3: 

This didn’t surprise me but should be a cause for concern as advances are made at faster rates and potentially less validation is made as organizations race to be first to market with some AI-based competitive advantage. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.

Bonus Item:

The discussion about the value of unstructured data was very interesting, especially when you consider:

  1. The potential for NLU (natural language understanding) products in conjunction with ML and AI.
  2. The importance of semantic data analysis relative to any ML effort.
  3. The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
    • I personally believe that the biggest exception to assertion this will be from GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond those of traditional CPU based products, and have geospatial capabilities that provide an additional dimension of insight to the problem being solved.

 

Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy

This is definitely an exciting space that will experience significant growth over the next 3-5 years. The more information, experiences, and lessons learned shared the better it will be for everyone.

The Downside of Easy (or, the Upside of a Good Challenge)

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Picture of a Suzuki motorcycle

As a young boy, I was “that kid” who would take everything apart, often leaving a formerly functional alarm clock in a hundred pieces in a shoe box. I loved figuring out how things worked, and how components worked together as a system. When I was 10, I spent one winter completely disassembling and reassembling my Suzuki TM75 motorcycle in my bedroom (my parents must have had so much more patience and understanding than I do as a parent). It was rebuilt by spring and ran like a champ. Beginners luck?

By then I was hooked – I enjoyed working with my hands and fixing things. That was a great skill to have while growing up as it provided income and led to the first company I started at age 18. There was always a fair degree of trial and error involved with learning, but experience and experimentation led to simplification and standardization. That became the hallmark to the programs I wrote, and later the application systems that I designed and developed. It is a trait that has served me well over the years.

Today I still enjoy doing many things myself, especially if I can spend a little bit of time and save hundreds of dollars (which I usually invest in more tools). Finding examples and tutorials on YouTube is usually pretty easy, and after watching a few videos for reference the task is generally easy. There is also a sense of satisfaction to a job well done. And most of all, it is a great distraction to everything else going on that keeps your mind racing at 100 mph.

My wife’s 2011 Nissan Maxima needed a Cabin Air Filter, and instead of paying $80 again to have this done I decided to do it myself. I purchased the filter for $15 and was ready to go. This shouldn’t take more than 5 or 10 minutes. I went to YouTube to find a video but no luck. Then, I started searching various forums for guidance. There were a lot of posts complaining about the cost of replacement, but not much about how to do the work. I finally found a post that showed where the filter door was. I could already feel that sense of accomplishment that I was expecting to have in the next few minutes.

Picture of a folded cabin air filter for a Nissan Maxima

But fate, and apparently a few sadistic Nissan Engineers had other ideas. First, you needed to be a contortionist in order to reach the filter once the door was removed. Then, the old filter was nearly impossible to remove. And then once the old filter was removed I realized that the length of the filter entry slot was approximately 50% of the length of the filter. Man, what a horrible design!

A few fruitless Google searches later I was more intent than ever on making this work. I tried several things and ultimately found a way to fold the filter where it was small enough to get through the door and would fully open once released. A few minutes later I was finally savoring my victory over that hellish filter.

This experience made me recall “the old days.” Back in 1989 I was working for a marketing company as a Systems Analyst and was given the project to create the “Mitsubishi Bucks” salesperson incentive program. Salespeople would earn points for sales, and could later redeem those points on Mitsubishi Electronics products. It was a very popular and successful incentive program.

Creating the forms and reports was straight forward enough, but tracking the points presented a problem. I finally thought about how a banking system would work (remember, no Internet and few books on the topic, so this was reinventing the wheel) and designed my own. It was very exciting and rock solid. Statements could be reproduced at any point in time, and there was an audit trail for all activity.

Next, I needed to create validation processes and a fraud detection system for incoming data. That was rock solid as well, but instead of being a good thing it turned out to be a real headache and cause of frustration.

Salespeople would not always provide complete information, might have sloppy penmanship, or would do other things that were odd but legitimate. Despite that, they expected immediate rewards and having their submissions rejected apparently created more frustration than incentive.

So, I was instructed to turn the dial way back. I let everyone know that while this would minimize rejections it would also increase the potential for fraud, and created a few reports to identify potentially fraudulent activity. It was amazing how creative people could be when trying to cheat the system, as well as how you could identify patterns to more quickly identify that type of activity. By the third month the system was trouble free.

It was a great learning experience from beginning to end. Best of all, it ran for several years once I left – something I know because every month I was still receiving the sample mailing with the new sales promotions and “Spiffs” (sales incentives). This reflection also made me wonder how many things are not being created or improved today because it is too easy to follow an existing template.

We used to align fields and columns in byte order to minimize record size, overload operators, etc. in order to maximize space utilization and maximize performance. Code was optimized for maximum efficiency because memory was scarce and processors slow. Profiling and benchmarking programs brought you to the next level of performance. In a nutshell, you were forced to really understand and become proficient with the technology used out of necessity. Today those concepts have become somewhat of a lost art.

There are many upsides to easy.

  • My team sells more and closes deals faster because we make it easy for our customers to buy, implement, and start receiving value on the software we sell.
  • Hobbyists like myself are able to accomplish many tasks after watching a short video or two.
  • People are willing to try things that they may not have if getting started would not have been so easy.

But, there may also be downsides relative to innovation and continuous improvement simply because easy is often good enough.

What will the impact be to human behavior once Artificial Intelligence (AI) becomes a reality and is in everyday use? It would be great to look ahead 25, 50, or 100 years and see the full impact of emerging technologies, but my guess is that I will see many of the effects in my own lifetime.