Strategic Account Planning Part 1

Strategic Account Planning Part 1

How Strategic Are your Sales People?  Here's a three question test to find out.

Just ask any salesperson if they work a strategic plan focused on developing their accounts and I will bet you a brand new nickel they will answer with a resounding YES!  Most will back up their story with lots of details on how they are exploring new opportunities, meeting new contacts, improving their business relationship and lots of other good stuff. No doubt, you will start nodding along. Perhaps you will be completely sold. Salespeople can be optimistically convincing. And, I like this quality in a seller.

But, I suffer from buyer’s remorse. A few weeks after being “sold” on the salesperson’s strategic plan, I ask for something relatively simple. Maybe it’s a sales projection for a new product, insight on the customer’s market position or thoughts on whether an OEM uses spare parts as a profit center. My inquiry could be just about anything, but the answer isn’t very satisfying. I need to be resold.

Do you ever find yourself in this position? If so, don’t feel like you are the only one. Sales managers from around the country candidly share their concerns: their teams don’t plan enough. More specifically, they rarely plan in a long term strategic way. Apparently, they too suffer from buyer’s remorse.

Over the course of our work with sales groups from dozens of distributors and manufacturers, we have developed a sure fire three question test for strategic planning at the account level. And because it’s a beautiful hot summer day here on the high bluffs of the Mighty Mississippi, I am going to share this test with you.

Question One: Tell me the plan for your next sales call at XYZ Company (which is in their top 5 accounts.) I want to know as much detail as possible. Who are you going to talk to? What are you going to talk about? Who else from our organization will be involved?

For most sellers this is easy. They have a strong idea of the next selling opportunity, what needs to be handled and often have plans for some sales ally or product specialist to be part of the mix. You will like the answer and the information will smoothly flow off their tongue. Perfect, now move the next question.

Question Two: Thinking about this same account, what will you be doing on a sales call in say 30 days? Again, give me as many specifics as possible.

If the salesperson has even a rudimentary plan, they will demonstrate how the first call is tied to this next customer interaction. Or, you may discover they are working multiple customer issues and this interaction some 30 days forward is another well thought out standalone event. In any event it will provide perception to short term planning at the account.

Experience dictates experienced proactive salespeople have a handle on their strategy a month out. Even the sales guy who merely reacts to customer emergencies and various product requests can bluff their way through this discussion.

Question Three: Looking again at this same account, what do you feel you will be working on in 120 days?

A plan for activities four months forward is an early litmus test to strategic planning. One would expect even a rookie strategic plan for an account would extend into the next quarter. Saying this, most salespeople will provide definite clues to their lack of planning.

What would a good answer be? Well, for one thing, a sign of a plan would be thoughts on expanding the business. A good response might go something like this:

“By the end of the calendar quarter we hope to position ourselves with the field service team of the customer. We want to gather information on the number of emergency field trips taken and the cash outlay for each of these trips. This will enable us to present our plans for a remote access system to management complete with financial data.”

Or the conversation might look like this:

“We want to strengthen our position with the customer by eliminating small vendors. Over the next few months I will be identifying products for conversion and presenting them to our current internal coach. I suspect that in four months we will have made identified a hit list and be ready to ask management to switch the business to our team.”

How do you get your team to think more strategically?
I believe we have to start by working to develop strategic plans for the top five accounts.

Analytics are for everyone !

Analytics are for everyone! Well, not building analytics, no. That needs a high level of expertise in statistics, machine-learning, optimization, programming, database skills, a healthy does of domain knowledge for the problem being addressed and a pretty wide masochistic streak too.

Using analytics, now that is for everyone, or at least it should be. We all use analytics, and, I think, the best examples, we use without thinking about just how complex it is.

Is there anyone out there that hasn't used an electronic mapping service (GPS) for directions? Even ignoring the electronics, these are remarkable pieces of engineering! An extensive, detailed database of road systems and advanced routing analytics to help you find the best route from A to B without sending you backwards down one-way roads or across half-finished bridges.

Perhaps you're thinking it's not that hard? Could you build it? What if I got the data for you? No? But you can use it right? They are not perfect, mostly I think because of data cleanliness problems, but they are close enough that I don't travel far from home without one.

More examples. Anyone used a search engine? How about an on-line weather forecast? How about web-sites that predict house-values? Recommendation engines like those used by Amazon and Netflix? All heavy analytic cores wrapped in an easy to consume, highly usable front-end.

These are, I think, among the exceptions in analytic applications - good analytics AND good delivery.

I talked about pseudo-analytics in a recent post: shams with no basis in science wrapped in a User Interface with the hope that nobody asks too many questions about what's under the hood. This is not good analytics.

Unfortunately even good analytic tools get under-used if they have not been made accessible to the poor people that have to use them. Spreadsheet tools probably top the list for unusable analytic applications: unusable that is by anyone except the person that wrote them. Sadly though, I have seen many examples both in reporting and applications where so little effort was put in to User Experience that any good analytics is almost completely obscured.

Building new analytic capability is a highly skilled job. Delivering analytic results in an easy to consume format so that it gets used is also a highly skilled and, frankly, often forgotten step in the process. After all we do build analytic tools so that they get used. Don't we? Sometime I wonder.

Next Generation DSRs - Bring the Analytics to the data

Under old world analytics, you move data from the DSR to your analytic server, build models, then write results (sometimes models too) back out for integration into the DSR.

Now, consider this:

• DSR datasets are often enormous. (2 years of data for a DSR I worked with recently input to a model was approx. 270 GB)
• Analytic tools are small. (The R base software, all 150 packages I have installed and the development environment is 625 MB)
• Analytic models are tiny. (Expressing a 10 component regression model in SQL, just 288 bytes and most of that is down to variable names)

Let's try that visually.

The input data is huge, everything needed to run R (my analytics tool of choice) is barely a blip on the scale and the resulting model can't be seen on this scale at all. And today we move the DSR data to the analytic server to run the analytics.... anyone else having an issue with this ?

Where the data is small enough that we can pull what we need via query over an ODBC connection and hold it in memory to run the analytics, perhaps you can live with the network overhead.

Similarly, if the DSR and analytic servers are co-located with a big fat data pipe connecting them, it doesn't matter so much. It's not same machine I'm after necessarily, but same rack would be nice.

What happens though, when the data is too big and the connection too slow (think wide area network) to be feasible? Now we need to build database structures on the analytic server, load the data (taking a copy), and if we are to re-run the analytics routinely, keep it in sync with the source on an ongoing basis. This is a lot of (non-analytic) maintenance work before we can even get started on the analytics.

So why do we do this?

"The analytic server is a high power, high memory machine great for analytics!" That's true but chances are your database servers have the same thing.

There are also valid concerns around how an analytic tool connecting directly to a database may impact other users. I do have a little sympathy for this, certainly much more than I used to, but think on this: a DSR is not a mission critical system. The failure of a mission-critical systems stops your business. If the DSR stops (and the chances are very good that you will have no issue at all), your reports are a bit late. Relax !

I have a suspicion that some of this is related to licensing. If you pay a small fortune for your analytic tools and they are priced per server, per CPU or per core, I can see why you would not want to go installing that software everywhere you might want to use it. Cheaper perhaps to bring the data to the software. Working with free open-source tools, it's not been an issue for me to install co-located or even on the same machine as needed.

Recently a number of database and BI vendors have moved to integrate analytic tools (often R, sometimes SAS) into their offerings trying to deliver real in-database analytics. I do think this is a great direction to move in though I have some concerns about the level of integration currently available. see my post on Analytic Power ! for more details.

Even if you can't execute true in-database analytics (which should be a Next Generation feature) there are still things you should be able to do to bring the analytics to the data.

First let's make a distinction between model-building (the act of creating new models from data) and model-scoring (running existing models against new data to make new predictions). All predictive analytic models I can think of can have this same split. (Descriptive and Prescriptive analytics do not)

Model-building is an intensive task, this is where all the heavy lifting happens in analytic work so processing and memory needs can be substantial though this varies widely depending on the analytic method and to some extent the implementation. If you have installed analytic tools directly on your database servers this may be enough to cause something of a slow-down. OK - try to co-locate instead. If you absolutely must replicate data to an analytic server on the other side of the world and try to keep your data in sync, I pity you.

Model-scoring is fast. A model is just a set of simple calculations. Deciding exactly what simple calculations you needed was the job of model-building but now you have done that, scoring new data against that model is quick.

This is what the result of a simple regression model looks like (in SQL):

[Variable_1] *-49.8916 + [Variable_2] *-24.2773 + [Variable_3] *-48.1305 + [Variable_4] -253.7238 + [Variable_5] *-20.7173 + [Variable_6] *17.722 + [Variable_7] *12.9865 + [Variable_8] *-17.4036 + [Variable_9] *2.2738 + [Variable_10] *-7.9186 + 6.668 AS Prediction

If you think it looks complex, look again, it's just a set of input variables multiplied by specific weights (as found by model-building) and then added together. This is easy work for the database. More complicated models will have more complex expressions, you may see logs, exponents, trig., perhaps an if..then..else statement. Nothing the database will find difficult to execute if it's expressed in the right language.

Unless models change with every input of new data (and so need re-building) there is no excuse not to score the model directly against the data. How you execute the model scoring is a different question and you have some options:

• you may load the model, new data and score directly in your analytic tools. This is using a sledgehammer to crack a nut, but it's easy to do if a little heavyweight/slow.
• for simpler models converting the model into SQL is not that difficult (though you do need to know SQL pretty well and have permission to build it into the database as a view, stored procedure or user defined function. This is probably the most difficult but fastest to execute.
• try converting the model to PMML (predictive model markup language) and use a server based tool designed to execute PMML against your database. (Many analytic tools have an option to export models as PMML.) A PMML enabled DSR would be a great enhancement for the Next Generation.

Bring your analytics to the data , spend more time doing analytics and less data time wrangling.

Frank on Building a Sales Process

We don’t normally post things like this, but recently River Heights Consulting’s Founding Partner Frank Hurtte was interviewed by Distribution Center Magazine. His informal interview was captured online and put out on podcast.

Listen in as Frank discusses Building a Sales Process here:

company law case study

Mr. Smith and Mr. Jones set up their business together as a partnership. Theirs is a highly sensitive professional business and privacy is particularly important to them. They are now considering whether to convert to LLP status. Being able to limit their liability to the capital they have put into the business is an attractive option. But they are also concerned about losing their current tax status and have heard that they will have to publish their annual accounts, which they are reluctant to do. They have a high degree of trust between themselves and so do not have a written partnership agreement.

So should they convert to LLP status?

LLP status would go a long way towards achieving their goal of limited liability. In order to reduce the risk of personal liability for professional negligence, they should also revise their terms of business and review their working practices, ensuring all liability remains with the business itself. However, because the risk of personal liability cannot be entirely eliminated under any type of company vehicle, they should assess the level of their professional indemnity insurance cover and think twice about taking on work that might lead to claims exceeding the amount of cover they have.

Smith and Jones should consider having a written members’ agreement to record at least the key terms of their business relationship, for example profit shares and decision taking. They should also give some thought to the consequences if one of them should cease to want to be involved in the business or become unable to work or even die. The law relating to traditional partnerships has developed over many years and there are rules applicable to most situations. But these rules mostly do not apply to LLPs, which makes it extremely important for LLP members to record the terms of their relationship as comprehensively as possible.

Smith and Jones will need detailed tax advice but, since members of an LLP are taxed in the same way as partners in a traditional partnership, the change to LLP status is likely to be tax neutral. Since their business also owns a freehold property, they can take advantage of an exemption from stamp duty if they transfer the property to the LLP within 12 months of incorporation.

LLPs must normally file annual audited accounts, but Smith and Jones may be able to take advantage of concessions available to ‘small LLPs’ if their business falls below certain thresholds relating to turnover, size of balance sheet and number of employees. In this case they need only file a simplified balance sheet, so their profit and loss account can remain confidential.

Finally, as members of an LLP, Smith and Jones would normally need to file details of their home addresses with the registrar of companies and these may be accessed by the public. There is, however, a provision in the Criminal Justice and Police Act 2001 for the Secretary of State to grant a confidentiality order allowing the home addresses of certain ‘at risk’ persons to remain confidential. If Smith and Jones are concerned about this then we can advise them on whether or not they would be likely to qualify for this protection.

Credit Lending Models

Micro finance institutions are using various Credit Lending Models throughout the world. Some of the models are listed below.  Associations : -------------------------------------------------------------------------------- This is where the target community forms an 'association' through which various micro finance (and other) activities are initiated. Such activities may include savings. Associations or groups can be composed of youth, or women; they can form around political/religious/cultural issues; can create support structures for micro enterprises and other work-based issues.  In some countries, an 'association' can be a legal body that has certain advantages such as collection of fees, insurance, tax breaks and other protective measures. Distinction is made between associations, community groups, peoples organizations, etc. on one hand (which are mass, community based) and NGOs, etc. which are essentially external organizations. Bank Guarantees : -------------------------------------------------------------------------------- As the name suggests, a bank guarantee is used to obtain a loan from a commercial bank. This guarantee may be arranged externally (through a donor/donation, government agency etc.) or internally (using member savings). Loans obtained may be given directly to an individual, or they may be given to a self-formed group.  Bank Guarantee is a form of capital guarantee scheme. Guaranteed funds may be used for various purposes, including loan recovery and insurance claims. Several international and UN organizations have been creating international guarantee funds that banks and NGOs can subscribe to, to on lend or start micro credit programmes.  Community Banking : -------------------------------------------------------------------------------- The Community Banking model essentially treats the whole community as one unit, and establishes semi-formal or formal institutions through which micro finance is dispensed. Such institutions are usually formed by extensive help from NGOs and other organizations, who also train the community members in various financial activities of the community bank. These institutions may have savings components and other income-generating projects included in their structure. In many cases, community banks are also part of larger community development programmes which use finance as an inducement for action. Cooperatives : -------------------------------------------------------------------------------- A co-operative is an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly-owned and democratically-controlled enterprise. Some cooperatives include member-financing and savings activities in their mandate. Credit Unions : -------------------------------------------------------------------------------- A credit union is a unique member-driven, self-help financial institution. It is organized by and comprised of members of a particular group or organization, who agree to save their money together and to make loans to each other at reasonable rates of interest.  The members are people of some common bond: working for the same employer; belonging to the same church, labor union, social fraternity, etc.; or living/working in the same community. A credit union's membership is open to all who belong to the group, regardless of race, religion, color or creed.  A credit union is a democratic, not-for-profit financial cooperative. Each is owned and governed by its members, with members having a vote in the election of directors and committee representatives. Grameen : -------------------------------------------------------------------------------- The Grameen model emerged from the poor-focussed grassroots institution, Grameen Bank, started by Prof. Mohammed Yunus in Bangladesh. It essentially adopts the following methodology:  A bank unit is set up with a Field Manager and a number of bank workers, covering an area of about 15 to 22 villages. The manager and workers start by visiting villages to familiarise themselves with the local milieu in which they will be operating and identify prospective clientele, as well as explain the purpose, functions, and mode of operation of the bank to the local population. Groups of five prospective borrowers are formed; in the first stage, only two of them are eligible for, and receive, a loan. The group is observed for a month to see if the members are conforming to rules of the bank. Only if the first two borrowers repay the principal plus interest over a period of fifty weeks do other members of the group become eligible themselves for a loan. Because of these restrictions, there is substantial group pressure to keep individual records clear. In this sense , collective responsibility of the group serves as collateral on the loan.  Group : -------------------------------------------------------------------------------- The Group Model's basic philosophy lies in the fact that shortcomings and weaknesses at the individual level are overcome by the collective responsibility and security afforded by the formation of a group of such individuals.  The collective coming together of individual members is used for a number of purposes: educating and awareness building, collective bargaining power, peer pressure etc. Individual : -------------------------------------------------------------------------------- This is a straight forward credit lending model where micro loans are given directly to the borrower. It does not include the formation of groups, or generating peer pressures to ensure repayment. The individual model is, in many cases, a part of a larger 'credit plus' programme, where other socio-economic services such as skill development, education, and other outreach services are provided. Intermediatories : -------------------------------------------------------------------------------- Intermediary model of credit lending position is a 'go-between' organization between the lenders and borrowers. The intermediary plays a critical role of generating credit awareness and education among the borrowers (including, in some cases, starting savings programmes. These activities are geared towards raising the 'credit worthiness' of the borrowers to a level sufficient enough to make them attractive to the lenders.  The links developed by the intermediaries could cover funding, programme links, training and education, and research. Such activities can take place at various levels from international and national to regional, local and individual levels.  Intermediaries could be individual lenders, NGOs, micro enterprise/micro credit programmes, and commercial banks (for government financed programmes). Lenders could be government agencies, commercial banks, international donors, etc.  Non-Governmental Organizations : -------------------------------------------------------------------------------- NGOs have emerged as a key player in the field of micro credit. They have played the role of intermediary in various dimensions. NGOs have been active in starting and participating in micro credit programmes. This includes creating awareness of the importance of micro credit within the community, as well as various national and international donor agencies. They have developed resources and tools for communities and micro credit organizations to monitor progress and identify good practices. They have also created opportunities to learn about the principles and practice of micro credit. This includes publications, workshops and seminars, and training programmes. Peer Pressure : -------------------------------------------------------------------------------- Peer pressure uses moral and other linkages between borrowers and project participants to ensure participation and repayment in micro credit programmes. Peers could be other members in a borrowers group (where, unless the initial borrowers in a group repay, the other members do not receive loans. Hence pressure is put on the initial members to repay); community leaders (usually identified, nurtured and trained by external NGOs); NGOs themselves and their field officers; banks etc. The 'pressure' applied can be in the form of frequent visits to the defaulter, community meetings where they are identified and requested to comply etc. Rotating Savings and Credit Associations : -------------------------------------------------------------------------------- Rotating Savings and Credit Associations (ROSCAs) are essentially a group of individuals who come together and make regular cyclical contributions to a common fund, which is then given as a lump sum to one member in each cycle. For example, a group of 12 persons may contribute Rs. 100 (US$33) per month for 12 months. The Rs. 1,200 collected each month is given to one member. Thus, a member will 'lend' money to other members through his regular monthly contributions. After having received the lump sum amount when it is his turn (i.e. 'borrow' from the group), he then pays back the amount in regular/further monthly contributions. Deciding who receives the lump sum is done by consensus, by lottery, by bidding or other agreed methods. Small Business : -------------------------------------------------------------------------------- The prevailing vision of the 'informal sector' is one of survival, low productivity and very little value added. But this has been changing, as more and more importance is placed on small and medium enterprises (SMEs) - for generating employment, for increasing income and providing services which are lacking.  Policies have generally focussed on direct interventions in the form of supporting systems such as training, technical advice, management principles etc.; and indirect interventions in the form of an enabling policy and market environment.  A key component that is always incorporated as a sort of common denominator has been finance, specifically micro credit - in different forms and for different uses. Micro credit has been provided to SMEs directly, or as a part of a larger enterprise development programme, along with other inputs.  Village Banking : -------------------------------------------------------------------------------- Village banks are community-based credit and savings associations. They typically consist of 25 to 50 low-income individuals who are seeking to improve their lives through self-employment activities. Initial loan capital for the village bank may come from an external source, but the members themselves run the bank: they choose their members, elect their own officers, establish their own by-laws, distribute loans to individuals, collect payments and savings. Their loans are backed, not by goods or property, but by moral collateral: the promise that the group stands behind each individual loan.

certificate of internship

CERTIFICATE OF SIX WEEK SUMMER TRANNING

This is certify that Mr. RaviRaj Prasad Kushwaha Registration No. 11200368 of Lovely Professional University Phagwara, Punjab (India) has successfully completed his industrial training in “Accounting & Finance”department in our organization for the period of 6 weeks during the training period from 01.07.2014 to 10.08.2014. His presence was excellence, His efforts towards his training was very appreciable. During this Training his performance and conduct was excellent.

“We wish him best of Luck for his future”

Marketing the Last Frontier

Marketing the Last Frontier

Marketing plays an important role in business. By definition, marketing works to

inform customers of the value created by an organization. Extending further, marketing also complements the sales department by acting as the mechanism for collecting customer data, determining why customers buy, developing a comprehensive value message and matching a company’s product/services to various customer groups.

In years gone by, Distributors sorely lacked marketing knowhow. When a marketing person was on staff, they were most generally described as event coordinators and keepers of the trinkets. I’m not saying they didn’t work hard. Nor, am I implying these early marketing folks weren’t an asset. But they certainly did little to actively direct the Distributor to higher sales or greater gross margin.

Happily, those days are gone.

Last week, I attended the National Association of Electrical Distributors (NAED) AdVenture Marketing Conference in Chicago. The event was a gathering of 200 plus professionals from the marketing departments of both Distributors and Manufacturers. And, these folks had a purpose: generate growth in the industry.

Top speakers covered topics ranging from Social Media to Amazon to going digital. My own presentation covered building a killer loyalty program. Without taking away from the speakers, the most exciting part of the whole gathering came via networking. I couldn’t help but notice the sharing of best practices. And, I noticed supplier marketing teams and their Distributor counterparts sharing ideas for future efforts.

Distributor Marketing is Accelerating
Failure to get your marketing effort off dead center will be harmful to your financial health. Just like the medical field of the 1800s, snake oil toting experts are beating the drum and passing themselves off as “Dr. Marketing.” There is no elixir of life or ancient cure-all remedy. Despite the bark of these later day charlatans, social media, search engine optimization, email marketing and CRM systems won’t magically heal your bottom line and cause your warts to vanish. It takes commitment, hard work and some planning for the future.

Here is a short marketing checklist:
• Customer segmentation – A lot of distributors break their customers down into a few categories for pricing, but put little thought into the basic differences in business operation. Thought must go into customer values, motivations and what the customer looks for in a supplier.
• Contact segmentation – Engineers think differently than maintenance people. Business owners respond to different messages than project managers. Provide the wrong information to a person and they are likely to tune you out.
• Branding message – Distributors used to rely solely on their supply partners for brand recognition. What do you want to be known for?
• Consistency of message – We just spoke to a Distributor who found themselves with 4 different versions of their logo. Another, Distributor has a different tag line on their line card than their website.
• Plan your programs – What happens when programs overlap? Customers are confused. Even your sales team has a hard time understanding which program carries the priority.
• Coordinate your programs with supply partners – When the distributor and their key suppliers work together it’s a thing of beauty. There should be ongoing meetings to understand how you can build synergy.
Finally…
• Website – We used to be able to build a website and then say we had one. But that’s not good enough anymore. If you website hasn’t been updated, overhauled or added to in the past 3-4 years you may be in trouble.

Next Generation DSRs - Analytic Freedom !

Current Demand Signal Repositories don't play well with others. Their data is locked away behind layers of security and you can only access it through the shackles of their chosen front-end for reporting. There is no good way to get that rich dataset into other tools: you have to copy it into a new database and new data structures. (In some cases you may have to do this twice, once to rearrange the data from the DSR into a format you can understand, then again to match the data structure needs of the downstream tool.)

For small-scale models (do we do those anymore?) that sip data from the original repository you can do this through the reporting engine and live with the pain, for large scale modeling it's really not an option.

I want freedom. Freedom to analyze with whatever tools I need: The freedom to report in Business Objects, visualize in Tableau, analyze in R and run existing applications (order-forecasting, master-data-checking, clustering, assortment optimization, etc.) directly against this data. (I'm not endorsing any of these tools and you can replace the named software above with anything you deem relevant - that's kind of the point).

Much of this freedom comes from a simplified data model, enabled by new database technologies (massively parallel processing, scale-out, in-memory and columnar). See more details at data handling.

It also needs a security model that is handled by the database NOT the reporting layer or as soon as you get to the underlying data you can see lot's of interesting things you shouldn't :-)

I suppose I could live with a little less freedom if a DSR offered all the tools I need but I don't think that's realistic. Not all DSR reporting layers are equal, data visualization is hit and miss, and as I posted in An Analytic name is not enough while there are some good DSR based analytic applications you will find many use pseudo-analytics and some have no analytic basis at all.

Do you think, perhaps, that the Next Generation DSR will provide the best reporting, visualization and analytic tools available? Sorry, I don't think so. DSRs cover a dizzying array of analytic need and developing robust, flexible analytic applications, even assuming easy access to the data, is an expensive proposition for any DSR vendor to do alone. I anticipate a few strong analytic "flag-ship" tools will emerge alongside more me-too/check-the-box applications packed with pseudo-analytics.

So, what can the Next Generation DSR do to help?

• make it (much) easier to get at the data in large quantities,
• make it (much) easier to bring analytics to bear on that data. (Perhaps with an integrated analytic toolset)
• open up the system to whatever analytic tools work best for you
• make it easy for other software vendors to provide add-in analytics on the DSR data/analytics platform.

Think about that last point for a moment, no DSR vendor is big enough to provide state of the art analytic applications in all areas, but make it easy enough to integrate with and it could enable specialist analytics vendors to offer their tools as add-ins to the platform. (This could be good news for the analytics vendor too, it removes the need for them to install and maintain their own DSR just to enable the analytics)

Let's look at an example.

Today if you want assortment-optimization capability, you can

• wait for your DSR vendor to develop it and hope they use real analytics; or
• search for another solution and work to interface the (very large) quantities of data you need between the applications;
• write your own (always fun, but you had better know what you are doing) and you will still need to interface the data.
• decide not to bother

All but the last one of these are slow - I'm guessing 12 months plus.

In the NextGen world, if you want to new analytic capability, you could still write your own, it's easy to hook up the analytic engine, or, just go to the DSR's analytic market-place and shop for it.

Next Generation DSRs - An Analytic name is not enough

You need not always build your analytic tools, sometimes you should buy in. If the chosen application does what you need that often makes good economic sense... as long as you know what you are buying.

Let's be clear, an Analytic name does NOT mean there are any real Analytics under the hood.

For many managers, Analytics is akin to magic. They do not know how an analytics application works in a meaningful way and have no real interest in knowing. At the same time, there is no business standard for what makes up "forecasting", "inventory optimization", "cluster analysis", "pricing analysis", "shopper analytics", "like products" or even (my favorite) "optimization".  Don't buy a lemon!

In the worst examples, there is nothing under the hood at all. One promotion-analytic tool I came across recently proudly proclaimed that you (the user) could calculate the baseline and lift for each promotion however you saw fit and then just enter the result into their system. They presented this as a positive feature, but calculating a meaningful baseline and lift is the difficult part!!

I've seen similar approaches for:

• off-shelf alerting tools that ask you how long of a period of zero sales is abnormal (so they can report exceptions)
• supply chain systems that need you to enter safety-stocks or re-order-points (so they can figure out when to order).  
• assortment optimization tools that want you to input product substitution rates.

Hmmm, is a car without an engine still a car?

Many applications use pseudo-analytics. After all, how hard can it be? "cluster analysis" , that's finding groups of things right? I reckon I can figure that out, no stats required. Yeah, right, of course you can... FYI - meaningful, useful clusters may be a little more difficult. It's not that cluster analysis is particularly hard, but neither is it something you can knock together without the right tools or any statistical understanding.

Sadly, I have seen real world examples of pseudo-analytics too in pricing analytics, off-shelf alerting, demographic analyses, inventory optimization and forecasting.

The right tool for the right job. There are many good analytic applications available, but you can still make it useless if it does not suit the task you have in mind. Using a time-bucket oriented optimization program to schedule production runs with sequencing comes to mind. OK, relatively few people are going to understand that one and it's not a DSR application, but it is real, the software vendor did not come out shouting that there would be a problem and 2 years down the line that project was abandoned.

Are DSRs worse than other applications?

I think this kind of feature-optimism, is a general issue in buying any analytic app but my perception is that it is a bigger problem in the DSR space.  Perhaps because the DSR is trying to offer so much analytic functionality to so many functional areas?  Is a DSR really going to handle forecasting, pricing-analytics, cluster-analysis, weather-sensitivity-modeling, promotional analytics, inventory optimization, assortment selection and demographic analysis (note - not a complete list), all as packaged software, for $50K a year?   Not unless they can scale that investment across a huge user-base.  Some will be good, others not so much - be warned.   

Spotting a lemon

An expert in the field (with analytic and domain knowledge) can spot a lemon from quite a distance. If you do not possess one you would be wise to invest in some consulting to bolster your purchasing team. For those applications that pass the sniff-test, the proof of any analytic system is in it's performance. Define rational performance criteria, test, validate, pilot and never, ever, ever rely on a software vendor ticking the box in your RFP.

Let the good times roll – growth and expansion create people issues

Let the good times roll – growth and expansion create people issues

According to a just released survey from the National Association of Wholesalers and accounting firm McGladrey LLP, distributors are experiencing robust sales growth. Actually, the numbers looked even better than one would expect; 74% had growth over the past 12 months and 91% expect to see growth in the coming year. The distributors came up with projections just shy of 9% projected increases ahead. All pretty darn good.

Here are a few factoids from a couple of the National guys:
• Wesco reports organic sales increases of 6% with Gross Margin holding in the 20.5% range.
• Lawson Products reports sales increases of 5.5%.
• Graybar, the electrical giant, reported a record first quarter.
• Praxair and Motion are talking 6% increases.
• Grainger was up 5%

Yes, this is a very rosy outlook for distribution.

Why do I share these numbers? I believe we all need to benchmark ourselves against the industry. It’s easy to say things are great because our numbers are up, but the whole market is up. You are most likely not growing your position in the market unless you are pushing double digit growth.

To sustain growth like this many distributors, including the big national guys, are adding people. According to the same survey, 66% of distributors are contemplating the addition of more people. Many of these positions will be customer facing positions.

Based on experience, finding good people is tough. Actually, much more difficult than you would imagine.

A couple of months ago we wrote a piece for Industrial Supply Magazine called “Hiring Right”.
You can read the whole thing here:  I want to make one point clear, everyone is on the hunt. Lots of people will be “fishing for employees” from your sales department. You may be thinking about going after some of theirs. I had this to say about “stealing” Sellers from the competition:

Credit: www.recruiter.com

Talking about this topic is both taboo and time honored in our industry. I would have to wonder if the practice really brings the “dreamy” results some people imagine. Experience dictates that many recruits actually disappoint their new employers. Promises of large customer followings and instant sales results fall unmet. Unless particular care is exercised, people recruited from competitive organizations bring along years of bad habits, unwanted supplier issues and potential legal issues.



I would like to leave you with three of additional thoughts:
• Let your salespeople know you appreciate them. Most people leave good companies because they are upset by something small and it gives them pause to consider other offers.
• Consider your current bench strength. It will take you far longer to find a new person than you can imagine. Start your process now instead of six months from now.
• Keep your eyes peeled for customers and/or supplier people who may be getting cut loose. Some of the Fortune 1000 companies are still shedding people for seemingly ridiculous reasons. There is a good chance of finding a keeper in there.

And even though it may seem self-serving and arrogant, I do recommend reading the article in Industrial Supply.