Chapter 23: Turning Soft Data Into Hard Data (And Ultimately Into Impact)

Soft data funnelUp to this point we have been discussing how to measure problems, how to define problems, how to determine what a favorable end result would be, what the value of that end result would be, and what that value would be over time. In Chapter 22: Turning Financial Analysis Into A Value Discussion, we took some very simple round numbers from Hard Data and had a discussion with our client to hopefully create an understanding that there is a problem worthy of a cost-effective solution. Ready to sell now? Hold on – there is another type of data that we need to talk about:  Soft Data.

Soft Data is by nature hard to quantify and put into a measurable ROI. If your customer wants to increase security in their building, create an environment that attracts better employees, elevate their position in the marketplace, or foster better teamwork, you are going to have a hard time turning this type of general statement into actionable data. While it will always be more difficult to uncover and quantify Soft Data and how it affects the problems and potential solution, there are ways to increase the clarity of the situation.

We all have heard, simply put, that customers buy to either increase the good or decrease the bad. Sometimes a solution will address just one of these, but more often it will impact both good and bad issues (for example, an exercise program will increase your fitness and decrease your weight). For our purposes here, let’s look at these as two distinct situations.

If your prospect is focusing on increasing good things, you will most likely hear phrases from them that talk about the goals, objectives, accomplishments, targets, or improvements that they desire to obtain. For example, they may say that they want to improve the security in their employee parking garage. How do you measure that to create an ROI? It starts with a series of questions:

  • “If you improved security in the employee parking garage, what would that do?” (It would make our employees feel safer when they work late and have to walk to their cars in the dark)
  • “If they worked late but felt safer when walking to their cars, how would that affect your business?” (Some of our high priority projects are in danger of slipping, and we need people to work longer hours to catch up…if they felt safer walking to their cars at night, they would be more likely to work later)
  • “And if those employees worked later more often, what would the impact be on those high priority projects?” (We would complete those projects on time and we would bring in an extra $XXX dollars over YYY months into the company)

A bit simplistic? Of course, but it illustrates the process of turning Soft Data into Hard Data and ultimately into Impact. What if your prospect is focusing on decreasing bad things? If so, you will most likely hear phrases from them that talk about pain points, concerns, margin slippage, process roadblocks, or work silos that they want to diminish. While it can be difficult to get a prospect to fully unveil all of their pain points, once they are discussed, these are usually easier to measure. Let’s use the same example, except this time the prospect is saying their problem is that people are unable to work late to finish some important projects:

  • “Why don’t your employees want to work late?”  (We had some cars broken into at our employee parking garage, and now people don’t want to be in the garage when it’s late, dark, or when they are alone)
  • “Did they work late in the past, and did they feel safe then?”  (Yes, we never had a problem with employees working late until we had the car break-ins)
  • “What will it cost the company if you don’t finish these important projects?”  (It will cost us $XXX dollars over YYY months)
  • “What else could happen if the employees don’t feel safe?”  (Morale will decrease because employees will feel management doesn’t care about their safety, and ultimately, we could get sued if an employee were attacked in the garage)

This line of questioning could go on, uncovering both Hard Data (those $XXX dollars over YYY months that have a financial impact) and Soft Data (decreased morale, potential for a lawsuit, difficulty in recruiting new employees, etc.). The point is that much of the data that you thought would be hard to quantify is really an issue of your ability to drill down with a series of questions that will turn that Soft Data into Hard Data, and ultimately into Impact. The goal of course is to walk down this path of discovery with your prospect, unearthing different types of data, quantifying it as best as you can, and then helping your prospect understand the problem. A side benefit is that this process will help your prospect explain these findings to his or her colleagues who may become a part of the final decision of moving forward with your solution.

What if you uncover issues that just can’t be quantified? We’ll cover that next in Chapter 24.

 

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Chapter 22: Turning Financial Analysis Into A Value Discussion

photo-30By now you may be chomping at the bit, wondering when you get to do your PowerPoint or demo.  After all, your competitors have already given their presentations, and here you are, still asking questions instead of talking features and benefits. Steady…stay on target…we’re setting the table here, not going straight to dessert.

In Chapter 21, we looked at how to turn soft, hard, or inferred data into a form of impact. The degree of financial analysis required will depend on your industry and the order of magnitude of the problem to be solved.  It is important to not get too bogged down in the early stages. Consider the problems of a traditional detailed financial cost analysis:

  1. Determine the project’s cost savings impact over 3-5 years (a guess)
  2. Determine the project’s cost over 3-5 years (another guess)
  3. Subtract #2 from #1 for the project’s total cost savings (guess – guess = guess²)
  4. Factor in the average weight of capital to the equation (the mother of all guesses)

That is a lot of guessing. And there are obviously many more steps than in this simplistic example; imagine how all of those guesses keep skewing the results! And what happens if you or your clients aren’t happy with the numbers you generated? Be honest…you most likely go back and tweak the numbers until you end up with something closer to what you were expecting.  Guessing and manipulation – that’s a recipe for disaster.

The art of getting useful results out of this process will depend heavily on how you take the exercise and turn it into a discussion, not a complete financial analysis. After all, your goal is to help your client understand the problem and its impact, help place you in the role of a trusted advisor, and of course, help you continue to qualify your prospect. Besides, if you are like most salespeople, you are not a spreadsheet genius who delights in the world of credits, debits, and pivot tables. Fortunately, if you can help your 4th grader with math homework, you have all the financial skills necessary for this phase.

In How To Take Data And Turn It Into Impact, we used the example of rekeying the locks at a university to illustrate how to create a very basic understanding of the financial impact of a problem. This type of financial analysis is obviously simple, often referred to as “back of the envelope” math. The exercise reduces the amount of guessing (or makes it obvious to you and your client that you are guessing and agree that you are using round numbers to get a general understanding of the problem). You are now in a position to take your ballpark numbers and talk about them in a way that not only starts to firm up your mutual understanding of the problem, but may also allow you to uncover additional problems that your company can solve.

You both need to work this together

Besides the obvious face time this gives you and the client buy-in it produces, it will give you additional insight into your client’s opinions, allow you to test your assumptions, and enable better, more educated guesses (yes, we’re still guessing at this point, but that’s okay). As you talk about the problem, you will need to put your findings into the language of money. After all, the solution you offer will be based on money. In the end, if your client can’t apply a viable ROI to your solution,  even the biggest problem won’t get the funding to solve it.

HP-12C

Yes, they still make the trusty HP-12C

Keep the math simple, and keep things centered on positive and negative numbers. This is not the place for percentages or ratios, and it certainly is not the place to show off your MBA skills with your trusty HP-12C calculator. If the numbers come up larger than expected, your client may start to distrust the process, even though you both contributed to that process. If this happens, take the conservative route  and say something like, “That seems a little high…is this what you were expecting?”

Pay careful attention here…you are about to either get solution buy-in or problem abandonment

If they answer that the number seems about right or might actually be underestimated, keep moving forward. If they answer that the number seems high, work with them until you both reach a conservative number that you not only believe, but can later prove to the client’s senior management who ultimately will write the check for your solution. Then ask one final question:  “Is this problem big enough to require a solution?” Because while your solution might solve a problem with an annual ROI of $100,000, that might mean nothing to a $50 billion company with other priorities.

If after all of your work together you get to the point where your client is not seeing numbers that justify moving forward, you need to take a deep breath and evaluate where you are. Should you keep pushing and try to get to a number that works? If you have been lazy with your qualifying over the past few meetings, you may be fooled into pushing forward. If you have been constantly qualifying your prospect throughout your entire engagement, you will probably see that you have reached the point where further time will not pay off. That’s a tough call, but an important one. Don’t keep trying to push that rope uphill. Move on.

But wait, not so fast. There are few absolutes in business. It may make sense to continue on with what appears to be an unqualified prospect. What if there is more to this problem’s ROI than can be easily measured financially? Just as there are soft costs, there is also soft data. We’ll look at soft data and how it impacts your client in Chapter 23.

Chapter 20: Helping Your Prospect Find Missing Data

In Chapter 19 we learned about the five types of data.  Knowing the different types of data (hard, soft, inferred, none, and fantasy) allows us to figure out if our prospect is dealing with real facts or not. Your first step is to ask questions to get a feel for just how solid those facts are.

Sometimes these can be easy softball questions (“You say that you don’t have a problem with shrinkage here at XYZ Retail Store…congratulations, that’s great, and very unusual!  Can you show me the audit records?  Maybe I can learn something here…”). Sometimes you may need more of a hardball statement (“You say you don’t have a problem with shrinkage here at XYZ Retail Store?  Really?  I’ve never heard of a client with zero shrinkage.  Why don’t we take a look at how the company has been auditing your inventory just to make sure…”).

How you ask questions about your prospect’s data depends on how deeply you have developed your relationship.  Obviously, a softball question is more appropriate for a first or second meeting – you can’t ask a hardball question right out of the gate unless you have established that personality trait as part of your hard-hitting, no-nonsense brand. Nobody expected that they would get easy questions when Mike Wallace showed up at their door, and if that is your image in your industry, feel free to charge ahead full speed.

Here’s the good news about data:  it either exists or it doesn’t exist:

  • What if the prospect has the data?
    • If it is soft or inferred data, can you help them solidify it with additional hard data?
    • If it is hard data, is it complete?
  • What if the prospect doesn’t have the data?
    • Does someone else have that data?  Can your prospect connect you to that person?
    • If nobody has the data, is there really a problem?
    • If nobody has the data, would your prospect like help in finding it?

You are not necessarily ahead of the game if your prospect has the data necessary to build a business case for your solution.  If you were not a part of the gathering process, you will never know how trustworthy that data is.  It is always a good idea, no matter how sure your prospect is, that you try to help them by furnishing additional data that you know is solid.  It will also serve as a reality check to see how the data that you bring is accepted by your prospect or his team.

When you bring any type of data into a team dynamic, it can be fascinating to see how different people react.  Put on your best x-ray glasses and look for those who may feel threatened that they are no longer the provider of data.  Healthy skepticism towards an outsider’s data is normal – unreasonable hostility to an outsider usually means problems for you down the road.

If your prospect doesn’t have the data necessary to build an effective business case for your solution, you are often better off.  First, if you bring or assist in bringing the data to the prospect, you will have a higher degree of faith that the data is accurate and useful. Second, if you both work together to find the data, you will be able to spend more time together and build a higher level of trust.  Collaboration is a much better way to work with a prospect, especially if the problem and solution is complex or technical in nature.

What if the client doesn’t want your help in obtaining the data?

This can be quite common.  Often, prospects can be a bit embarrassed when they realize that they were about to spend money on a project that is not supported by enough facts to create a solid business case.  They may push you off and say that they will go and get the data.  This can bring you back to square one, wondering if they are gathering hard, soft, inferred, or fantasy data.  You need to delicately push to assist them in this important part of the process.

You can help them realize that they need your help with a carefully worded question.  In the past, when I have encountered a prospect who didn’t want help finding or creating the necessary data, I have said something like, “I’m glad we agree that it is important to get this data before proceeding.  It’s great that you can go and get it now.  But I have to ask, if it is so important, why don’t you already have it?  It sounds like this may take some digging…I’d love to help so you don’t get too bogged down by this…”  Careful here – you need to walk that razor’s edge between being helpfully insistent and insulting.

What if they still want to do it themselves?  Enter the deadline statement: “No problem. To keep things moving, how about we set a date to review the data you are getting.  If you don’t have it by then, lets agree that at that point I’ll jump in and give you a hand.”

This can be very time consuming.  But it is time worth investing, as there is the possibility that you can use some of the data gathered with other prospects (stripped of anything that identifies your current prospect, of course) in similar industries as inferred data. More importantly, this is a great way to keep qualifying your prospect.  After all, is it really a good use of your time to work on a project with someone who is not concerned with supporting a business case for your solution with hard data?  That would only increase the odds of the solution failing to solve the real problem, or having the project cancelled before it starts because there was no convincing data that the solution would have an impact on the problem.

Chapter 19: Hard Data, Soft Data, Inferred Data, And Fantasy Data

As you help your prospect Confirm The Business Case, you may become frustrated to learn that she has been operating her division without much data on the problem at hand. In fact, you may learn that she has been working with no data or even wrong data.

Is that a problem?

Not necessarily. Some of the best time you can spend with a prospect is time discovering together what is real and what is not. Because you are a part of this discovery phase (and presumably your competitor is not, as he is just responding to an RFP), you can use this time to show your expertise, integrity, and desire for an optimum solution. Instead of handing over a 30-page proposal or clicking through a 2-hour slide deck, you are showing that you can be a long-term partner who will be an asset on not just this project, but on others in the future.

Who knows…you may just uncover the need for a much larger solution than originally planned.

Some of the facts needed to confirm your prospect’s original business case will be easy to understand. There are probably plenty of straightforward metrics to show that the old servers are slower, that new copy machines use a less expensive toner, or a new automated payroll system will reduce headcount requirements. Your prospect has probably already used this data as part of her own business case creation and ROI calculation. Ultimately, at some stage someone in senior management will ask something like, “Why should we spend money on this?” That is a not-too-subtle code for, “This may solve your problem, but what does it do for me?” The hard costs mentioned above may not be compelling enough for each person involved in the decision process for your project.

There are five types of data that you will need to address, and we will use a retail store for our example:

  • Hard data – often found in the finance department. For example, a store could perform an inventory and find that over the past six fiscal years they have experienced 5% shrinkage (a retailing industry term meaning, in our example, that the clothing store lost 5 out of every 100 sweaters they sold due to shoplifting or employee theft).
  • Soft data – often anecdotal, word of mouth, or from general statistics. For example, loss prevention specialists have historically told retailers that they will experience a shrinkage rate of 3%. This comes from years of studies over many companies, and can be used to help establish a standard of expectation.
  • Inferred data – often confused with soft data, it is instead a more focussed version of it. For example, the 3% shrinkage rate has been pulled from years of studying all kinds of retail stores. But this generalization may not apply for a consumer electronics store or a shoe store.
  • No data – not necessarily a bad thing, as discussed above. For example, our store may know nothing about their shrinkage rate because they have never performed an accurate inventory before. We can start at the ground level to help build the business case (and qualify the prospect).
  • Fantasy data – the worst kind of all! It is surprising how many prospects I have worked with who “believe what they want to believe” and disregard the hard, soft, or inferred data that doesn’t line up with the project they are working on. For example, the store may believe they have no problem with shrinkage, no matter how unlikely that may be.

Your prospect may believe that all of her data is hard data. It will take a bit of time and finesse to soft-pedal a quick lesson in the types of data that she really has versus what she thinks she has. Once your prospect understands this, you can begin the process of turning the other types of data into hard data. Yes, this will create extra work for you, but the relationship benefits that the extra effort creates will help you continue to qualify the opportunity, build additional trust, and keep your less involved competitors at bay.

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