The New Salesman: Success Expectations

In our first article in this series, we said product knowledge was the bedrock of selling. From this sound foundation flows the ability to present solutions, grow enthusiasm, and drive sales. Like a fresh spring in the desert wilderness, product knowledge pushes to the surface. And, just like the spring water, the most productive flows are channeled to the place where they do the most good.

In sales, a well-developed process serves to direct product knowledge to the most productive utilization. Think of this, unless the product knowledge is presented to the right customers, with a specific set of corresponding needs and an appreciation for the value provided by the seller, resources are wasted. Miss the mark with any of these and the sales growth is compromised.

 

Build accelerating factors into the stream of salesperson activities and the sales number grows faster. Insert habits which improve the probability of closing a higher percentage of orders result.

 

As we move forward in this series we will discuss targeting in great detail. But before we get to that, we should discuss some of the fundamentals of setting expectations.

 

Far too often, new salespeople are turned loose on their territory and I am called in some time later to figure out why they aren’t performing. Typically the sales manager is at wits end, they start off with a litany of things the seller is not doing and end by asking for my suggestions for improvement. The sales numbers are not growing, but that’s just a symptom of the real story. Simply put the new person is not executing their job correctly – they’re not meeting expectations.

 

I believe much of selling is activity based. There’s nothing highly magic about the situation. Give me a bright person and let me load them with the right activities and they have a pretty good chance of success. My first batch of expectations is purely activity based.

Here are my own expectations, yours might be different. Either way, you need to sit down and make a list of these to insure there is no miscommunication.

 

Expectation 1: Call me old-fashioned, call me puritanical, but I believe salespeople must be at work before 8:00 AM. With the move to more home flex offices, this is difficult for some people to handle. At work before 8:00 AM doesn’t mean rolling out of bed and sitting down to check emails at 8:01. It certainly does not include dropping the kids off at school at 8:25. Sound strange, but I keep hearing the same story.

It’s not a generational thing. I knew guys who went through this back in the 1980s. They all had good excuses. They knew somebody who was successful in spite of relaxed work hours. But, I suggest the new guy have expectations laid down on the first day.

 

Expectation 2: First appointment should be made for no later than 8:30. Actually this is a double expectation. First I expect appointments to be made – no drive by sales guys. Secondly, one of the biggest issues with new salespeople comes with getting stuck in the office. They start the day off on time and on schedule with a 7:30 arrival, grab a cup of coffee, and then do a few reports. Customers call in, they get stuck doing reactive work, which could have easily been handled by their support staff. The next thing they know it’s lunch time.

The corollary to this expectation is the expectation that they have an appointment at 3:00 as well. Blocking out the day with two proactive and planned activities pushes people to a whole new zone.

 

Expectation 3: Salespeople take notes. I like notes in a composition book which are difficult to pull pages out of. The salesperson should have the composition book with them always. Phone notes, customer visit notes, sketches of proposed products, new contacts met and everything else is in that book.

 

Expectation 4: The notes are either logged into the company CRM system or reviewed weekly to make certain that every commitment, customer request and anything else that came up has been handled. I have a method for dealing with this information that was taught to me by my boss back in the 80s. You probably have one two. The point is set expectations.

 

Expectation 5: Communications are critical bit of the selling business. Yet, new salespeople still struggle to return phone calls, answer emails or get quotes back to customers on time. I believe you should think about how much time is spent from initial customer call to call back. I believe the answer should be no more than a single business day. Four hours works better. The point is to lay down these expectations.

 

Expectation 6: Every sales call has a purpose. Howdy, dowdy (where you just stop in to say hi) calls don’t cut it.

 

Expectation 7: The new salesperson should know who is responsible for generating quotations. This varies by company, but it’s not uncommon for a new sales guy to waste her time developing a quotation just to show off technical prowess. Conversely, quotes are delayed because the salesperson thought somebody else was going to take care of them.

Hopefully, you are starting to get my drift on setting action based expectations. I have over 50 expectations for new sales folks. You may have more or less, but do understand these aren’t passed down by osmosis.

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Business Analytics - finding the balance between complexity and readability

In this blog I try to present analytic material for a non-analytic audience.  I focus on point of sale and supply chain analytics: it's a complex area and frankly, it's far too easy whether writing for a blog or presenting to a management-team to slip into the same language I would use with an expert.  

So, I was inspired by a recent post on Nathan Yau's excellent blog FlowingData to look at the "readability" of my own posts and apply some simple analytics to the results.

I've followed Nathan's blog for a couple of years now for the many and varied examples of data-visualization he builds and gathers from other sources. One that particularly caught  my eye was this one published by the  Guardian just before the recent State of the Union address in the United States (click to enlarge).

The Guardian plotted the Flesch-Kincaid grade levels for past addresses. Each circle represents a state of the union and is sized by the number of words used. Color is used to provide separation between presidents. For example, Obama's state of the union last year was around the eighth-grade level, and in contrast, James Madison's 1815 address had a reading level of 25.3. 

Neither the original post nor Nathan's go into much detail around why the linguistic standard has declined.  Within this period, the nature of the address and the intended audience has certainly changed.   Frankly, having scanned a few of the earlier addresses I think we can all be grateful not to be on the receiving end of one of them.

 So, I was inspired to find out the reading level of my own blog.  It's intended to present analytic concepts to a non-analytic audience.  I can probably go a little higher than recent presidential addresses (8th-10th grades, roughly ages 13-15) but I don't want to be writing college-level material either.

All the books my kids read are graded in this (or a very similar) way but I had never thought about how such a grading system could be constructed.   The Flesch-Kincaid grade level estimate is based on a simple formula:

That's just a linear combination of : 

• average words per sentence;
• average syllables per word
• a constant term.

In fact (though I have not yet  found details of how it was constructed) it looks to be the result of a regression model.  (Simple) data science in action from the 1970's.

Note that Flesch-Kincaid says nothing about the length of the book or the nature of the vocabulary it's all down to long sentences and the presence of multi-syllabic words. 

(BTW - the preceding sentence has a Flesch-Kincaid grade score of 13.63, calculated with this online utility).  Now that's pretty high, worthy of an early 1900's president and (supposedly) understandable by young college students.    The sentence is longer than typical; 31 words vs. my average of 18 (see below) and words like "vocabulary", "sentences" and "multi-syllabic" are not helping me either.

Approach

I could have used copy/paste into the online utility I used above, recorded the results in a spreadsheet and pulled some stats from that. That would work, but if I ever want to repeat the exercise or modify it, perhaps to use a different readability index, I must do all that work again.   At the time of writing, there are currently 44 published posts on this blog - there must be a better way.

Actually there are probably many better ways but as I also wanted to flex some R-programming muscle I built a web-scraper in R to do the work for me and analyze the results (more on this in a later post).

Results

Let's start with some simple summaries of the results I collected.

Histograms showing the % of posts from this blog (prior to 2/14/13), the average (mean) value shown in red. There is some variety in the grade reading level indicated by Flesch-Kincaid for my blog posts, averaging around 10 but ranging from 7 through 14.  I average about 750 words, but occasionally go much longer and have a number of very short "announcement" style posts.  Average words per sentence of 18.

OK, so now I know, but is that good?  I don't know that I have a definitive source but according to at least one source  the target range on  Flesch-Kincaid for Techical or Industry readers is 7-12, so I'm feeling pretty good about that.

I did wonder whether there was any other, hidden, structure to the data though.  I know the equation is based on words per sentence and syllables per word so there is no point looking at those, obviously I'll find a relationship.   But is my writing style influenced by anything else?

Flesch-Kincaid grade level vs. the number of words by post on this blog.  Other than a handful of long posts that rate lower in the range 8-10,  I don't see much going on here.

Flesch-Kincaid grade level vs. the publication date by post on this blog.  The size of each post (in words) is shown by the area of each point, color is used purely to help visually differentiate each of the points.  Apart from a couple of recent "complex" posts  this does seem to be showing a trend, so I added a regression line and labeled the more extreme posts.  Point (b) is a very short "announcement" style post (you can hardly see the point at all) and I could probably ignore it completely.  Point (e) is a more fun piece I did around using pie-charts that's probably not very representative of the general material either.

If you want to compare readability for yourself here are the top (and bottom) posts ranked by Flesch-Kincaid grade level

Rank	Post	Flesch-Kincaid grade level	words	sentences
1	Analytic tools "so easy a 10 year-old can use it"	13.3	784	33
2	Point of Sale Analytics - newsletter released	13.1	82	4
3	Point of Sale Data – Category Analytics	12.8	676	29
4	How to save real money in truckload freight (Part I)	12.8	723	31
5	The Primary Analytics Practitioner	12.7	541	29
6	Reporting is NOT Analytics	12.4	891	43
7	Point of Sale Data – Sales Analytics	12.1	478	24
8	Data handling - the right tool for the job	11.9	762	38
9	Data Cleansing: boring, painful, tedious and very, very important	11.8	297	16
10	Point of Sale Data – Supply Chain Analytics	11.6	958	41
35	The right tools for (structured) BIG DATA handling	9.0	1878	114
36	Better Point of Sale Reports with "Variance Analysis": Velocity...	8.9	1264	78
37	Better Point of Sale Reports with Variance Analysis (update)	8.5	177	10
38	Better Business Reporting in Excel - XLReportGrids 1.0 released	8.4	70	5
39	What's driving your Sales? SNAP?	8.3	651	42
40	Do you need daily Point of Sale data?...	8.2	1395	83
41	SNAP Analytics (1) - Funding and spikes.	8.1	531	32
42	SNAP Analytics (2) - Purchase Patterns	7.9	773	44
43	Business Analytics - The Right Tool For The Job	7.6	483	36
44	Are pie charts truly evil or just misunderstood ?	7.1	1097	70

Conclusions

It appears that my material is (largely) written at a level that should be accessible to the reader.  And I am using more readable language in recent blogs which sounds like a good thing.

But there remains a key question for me that these stats can't really answer. Am I getting better at explaining the complex (my goal) or just explaining simpler things ? What do you think ?

In case you are wondering, this post has a Flesch-Kincaid grade level of about 8.  So if you can follow the "State of the Union" address you should have been just fine with this.

The right tools for (structured) BIG DATA handling (update)

A couple of weeks ago, I ran a somewhat rough benchmark to show just how much faster large database queries can run if you use better tools.

The right tools for (structured) BIG DATA handling  Here's the scenario: you are a business analyst charged with providing reporting and basic analytics on more data than you know how to handle - and you need to do it without the combined resources of your IT department being placed at your disposal.  Sounds familiar?

I looked at the value of upgrading hard-drives (to make sure the CPU is actually busy) and the benefit of using columnar storage which let's the database pull back data in larger chunks and with fewer trips to the hard-drive.  The results were ..staggering.  A combined 4100% increase in processing speed so that I could read and aggregate 10 facts from a base table with over 40 million records on my laptop in just 37 seconds.

At the time I promised an update on a significantly larger data-set to see whether the original results scaled well.  I also wanted to see whether query times scaled well to fewer facts.  Ideally querying against 5 facts should take about 50% of the original 10 fact aggregation queries.

Test environment

My test environment remains the same,  a mid-range laptop, quad-core AMD CPU, with 8 GB of RAM running Windows 7 (64 bit) and with a relatively cheap (<$400) fast solid-state drive.

This time though I increased the data-quantity 10-fold to 416 million records

Then I ran the same aggregation SQL to pull back summaries of 10 facts from this table.

SELECT Item.Category, Period.Year, SUM(POSFacts.Fact1) AS Fact1, SUM(POSFacts.Fact2) AS Fact2, SUM(POSFacts.Fact3) AS Fact3, SUM(POSFacts.Fact4) AS Fact4, SUM(POSFacts.Fact5) AS Fact5, SUM(POSFacts.Fact6) AS Fact6, SUM(POSFacts.Fact7) AS Fact7, SUM(POSFacts.Fact8) AS Fact8, SUM(POSFacts.Fact9) AS Fact9, SUM(POSFacts.Fact10) AS Fact10 FROM Item INNER JOIN POSFacts ON Item.ItemID = POSFacts.ItemID INNER JOIN Period ON POSFacts.PeriodID = Period.PeriodID GROUP BY Item.Category, Period.Year

I repeated this timed exercise 5 times for:

• standard (row-based) SQL Server 2012
• SQL Server 2012 with the ColumnStoreIndex applied
• InfiniDb (a purpose built column-store database)

Finally I ran it again on each configuration but just summarizing for 1 fact:

SELECT Item.Category, Period.Year, SUM(POSFacts.Fact1) FROM Item INNER JOIN POSFacts ON Item.ItemID = POSFacts.ItemID INNER JOIN Period ON POSFacts.PeriodID = Period.PeriodID GROUP BY Item.Category, Period.Year

Results

Before we get to the query timing let's look at what was happening to my machine while queries were running.  

The first screen-shot below (click to enlarge) was taken while running queries with base SQL-Server (no column store indexes).  You can see that the CPU is just not busy.  In fact it's averaging only 30%  and that's with the solid-state disk installed.  The drive is busy, but only serving up about 50MB/s.  (I say "only" but of course that's much better than the old hard-drive.)

System resources running base SQL Server query

The next screenshot shows system resources while running a query with the ColumnStore Index applied.  The CPU is now busy on average 77% of the time and peaking at 100% on occasion.  The disk utilization chart may be misleading because it's now plotted on a much larger scale but the same disk is now hitting 200MB/s.  I think we can expect great things !

System resources running SQL Server with the ColumnStore Index

So, on to the timed results.  I ran each scenario 5 times and all results were very consistent, within +/-10% of the average.

Test results against 416 million records

Again, SQL Server 2012 with the Columnstore Index is the clear  winner.  Just 217 seconds to aggregate all 10 facts and, amazingly, just 21 seconds to aggregate 1 fact across the same 416 million records.  InfiniDb takes over twice as long against 10 facts and does not scale nearly as well with the single fact query.


Now compare with the results we got last time to see how well each database scaled with the increase in data volume.

Data volume increase by a factor of 10 and:

• InfiniDb and base SQL server both increased query time by about a factor of about 10, roughly in proportion.
• SQL server with the ColumnStore index only increased by a factor of 5.9 !   

To be fair I am comparing the (free) community edition of InfiniDB against (decidedly not free) SQL Server and neither tool is really intended to be run on a laptop.  But if you need rapid aggregation of data and do not have access to a cluster of commodity servers - it is clear that columnar storage helps you get that data out fast.

The other thing you may want to consider is that it took me substantially less time to load the data into InfiniDB (sorry I did not time it but we're talking minutes not seconds), and building that ColumnStore index in SQL actually took longer than the base query ~ 4500 seconds.  You may not want to go to this trouble if you just need a couple of quick aggregations.

Remember also that the table with the ColumnStore index is read-only after the index is applied. Want to make some updates?  That would be easier in InfiniDB.

Conclusions

Ultimately I'm not trying to sell you on either option, but if you have a lot of structured data to feed your analytic project, a columnar database may well be the way to go right now.  

The New Salesman: Growing the Territory

Accelerating Territory Growth One Individual at a Time 

One first impression,one client at a time.

Building a territory is like building a network of friends.  Let’s think for a moment about the friendships we have nurtured over the years.  If you’re like me, you didn’t just wake up to discover a pile of friends under the Christmas tree.  Instead, they grew based on shared experiences, time together and commonality of background. 

Amongst your best friends are classmates, coworkers, neighbors and people met through some service group.  You met them, you spent time together, or you went through some joint activity.  Maybe you shared the mutual burden of an overly demanding professor.  Or you worked together on a difficult fund raiser at your church.  The point is you intermingled in a series of joint activities.  Let’s drill deeper.

 Your timeline for establishing a relationship may have looked like this:

• Met at some activity or were introduced by mutual friends
•  Found some common ground
• Shared time together over a series of days, weeks, months or maybe years
• Developed trust
• Exchanged more information
• Developed a deeper relationship
• Established a long term friendship

During each step of the process, you learned more about the person.  And they learned just a bit more about you. 

For a new salesperson, developing relationships within their territory is similar to building a friendship.  Given time and a degree of luck, the newbie will build relationships in their assigned territory.  Some of these relationships will blossom into business alliances.  Some will grow into deeply seated lifelong friendships.  When a person matures along with their territory a magical thing happens.  This friendship factor is one of the true joys of being a seller. 

But this isn’t about eventually building friendships.  Instead, the purpose of our exercise is accelerating the process.  Quickly building strong relationships is the main point of the plan.

There is only one First Impression

The new salesperson is struggling with many things.  Product skills, new company culture, a fresh set of expectations and meeting many new people.  This is a daunting list of distractions; it’s easy to send the wrong message. And, the list is important.  So, what can a person do to create the right impression? 

Every salesperson brings a background and a set of strengths to their first call.  Yet most really don’t think of how they explain their strengths to their new customer.  I have cringed in pain while new sellers with years of engineering background struggled to present their value to the customer. 

Good sales managers coach their newest team member on what makes for a good self-delivered introduction.  Assume that most people are a bit shy about telling their own story.  They fear sounding boastful and throughout their careers may have never been asked to deliver their own personal elevator pitch. 

Step One – Build a personal story to share your background.

But the first impression goes far beyond just standing in one place and rattling off a 30 second commercial on your life’s experiences.  It can’t be one way; people are interested in those who show mutual interest.   This brings us to the second part of building the first impression. 
 

Good questions open doors.  And, with all the distractions of a brand new territory and a batch of new people, we can’t count on listening and thinking of questions at the same time.  Develop a list of introductory questions for the customer.  We will talk more about questions in another article, but for now let’s talk about starter questions.

Here is a couple to give you a flavor:

·         What is your favorite way of getting product information?
·         I see your job description is maintenance manager; can you give me an idea of what your duties cover?
·         What do you expect from one of your top supply partners?

We won’t waste time with the whole open ended question lecture.  There are other resources for that skill, but do not that each of these questions allowed the customer the opportunity to help you do a better job. 

Step Two – Create questions prior to your first visit.

I recommend making the first meeting entirely about the customer.  Your job is to gather information, make a good first impression and open the door for future sales.  But remember the Boy Scout Moto.  Be prepared.

 Have the following at your disposal:

·         A company line card
·         A “composition book” and a pen for taking notes
·         A history of previous sales to the customer (if applicable)
·         An understanding of any issues which may still be lurking in your customer’s mind from previous experiences.

Step Three – Be Prepared
You’ve met the customer, but the first impression continues far beyond the twenty minutes you spend together.

Make yourself different

There are thousands of new salespeople cruising around the planet.  A good many are trolling around your territory.  But you’re different.

The best way to jump start a relationship is to let the customer know you value their time and you value their opinion.  I recommend sending each new customer contact a thank-you note following your first visit.  Pay attention.  I did not say thank you email.  Send an honest to goodness card that says something like this:

John,

Thanks for sharing your time with me yesterday.  I really appreciate that you were willing to invest in our future.

Your opinions on the value of a good supplier made a bigger impact than you might imagine.  I have been thinking about what you said for the past couple of days.

Imagine this, the customer arrives at work and finds the card on his desk.  You took time to think of him.  Now he stops his work and reflects back on your time together. 

 

It’s a memorable first impression.

There are many other relationship accelerators.  We’ll hit them another time. But for now think about stepping off on the right foot. 

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