Category Archives: Sales Analytics

« Older Entries

Sales Forecasting Pain and Success: It Starts with the Process

Wednesday, December 2nd, 2009

For most organizations, sales forecasting is a time-consuming painful process that results in very little value. As stated in a blog I recently read, the typical process goes something like:

  1. Collect the forecast from sales reps, and assemble in a spreadsheet
  2. Roll-up forecast to managers who apply their subjective judgment
  3. Roll-up manager forecast to VPs who apply their own personal bias
  4. Roll-up the VP forecast to executives, who change it because it is too low
  5. Hand over the forecast to operations and finance who toss it in the garbage because they don’t trust it and it’s not timely enough to make an impact to production or planning.

(more…)

Tags: , , , , , ,
Posted in Sales Analytics, Sales Forecasting | No Comments »

A Well-Rounded Sales Forecast

Friday, November 6th, 2009

Hybrid or SUV?

Imagine being a fly on the wall in the Chrysler board room in 2001. Margins were jumping as sales of SUVs boomed; times were good. If management used a statistical forecast based on the previous years’ shipments to predict the future, they would be told one thing:  Build more SUVs!

We all know the story: 5 years later (about the time it takes to get a car from design to market), Toyota Priuses were selling like hot cakes and Chrysler was heading toward bankruptcy. Clearly, running your business on a statistical forecast could cause you to miss future trends.

It would be equivalent of driving by looking in the rear view mirror.

(more…)

Tags: , , , , , , ,
Posted in Right90, Sales Analytics, Sales Forecasting | No Comments »

CIOs Are Relevant Again

Friday, October 30th, 2009

I found some great observations in this post about why CIOs are becoming relevant again.

You could argue that a parallel process has been happening in the manufacturing world for some time and with the same effect. As companies that for years were running their own factories and infrastructures, moved to an outsourced model, the operational leaders needed to adapt. No longer was the operational executive as concerned with the internal operation of the infrastructure but rather, with the changing relationships and complexities with the ecosystem of partners and systems that they had established.

In the software world, a move to cloud computing is very similar. While a company may transfer responsibility of a system to a partner or group of partners, the burden of that system and how it impacts the business of the company is not relinquished and must be managed. The simple act of an upgrade or the unanticipated retiring of a feature could have profound implications to the company’s use of the software or how it integrates into a larger group of systems. While on-premise ownership affords a CIO the luxury of denying or delaying an upgrade for simplicity sake, the cloud offers no such shelter. This, by example, offers insight into the problems now faced by an organization in it’s move to the cloud. Like the operational transition in manufacturers, the end result is a new (slimmer) technical organization with similar skills and new responsibilities.

As the leader of the technical organization, the CIO must be charged with making sure this new business paradigm does not impact the company in a negative way. This has been the case in manufacturing and the operational executive has been the one to manage the organization through the transition. The role of the executive evolved – CIOs are more relevant than ever.

Posted in Sales Analytics, Sales Forecasting | No Comments »

A Downturn is Not the Time For Caution

Thursday, October 22nd, 2009

“I should have seen the crisis coming earlier.” – Jean-Paul Agon

I was reading a recent interview in the Wall Street Journal with L’Oreal CEO Jean-Paul Agon. The article’s beginning states “First-half net profit at the world’s largest cosmetics company fell 14% after decades of at least 10% annual increases…Chief Executive Jean-Paul Agon is scrambling to fix L’Oréal.”

I was not surprised to see later in the article that Algon said “This year I’m very cautious. I learned my lesson.” L’Oreal management, like most companies, was surprised by the severity of the recession and vast impact on the world’s economies and on their product sales.

The question I ask now is: Will “being cautious” slow the recovery of his company and to a greater extent the economies of the rest of the world? Possibly. Not because I am overly optimistic (being a “finance guy” that isn’t actually possible), but because this isn’t a time for executives to over-react.

My position is to get the right data and let the data provide the guidance. The best data is going to come from our sales and marketing teams—not those historical models based on shipments that have been used by operations to build inventory. I think this was the key data input that was missing to L’Oreal and its executives. With good bottoms-up sales forecasting, they may have picked up the early signals leading to a significant downturn with enough time not to be caught by surprise. Picking up the signals leading to an upturn will also ensure they fully take advantage of the inevitable upturn when it occurs.

If you connect your real-time sales forecast to your production operations, you will reduce inventory, obsolescence, stock outs and improve on time delivery, customer satisfaction and financial efficiency. I think an automated sales forecast could help Mr. Agon scramble less in times of great volatility.

Tags: , , , ,
Posted in Sales Analytics, Sales Forecasting | No Comments »

Income Shouldn’t Soften the Blow of a Missed Forecast

Wednesday, October 14th, 2009

“Dear Stockholder: We missed our revenue forecast, but it is okay because we made more income. Please don’t be mad.” Is this really an acceptable position for companies to take? Why do companies miss their own revenue forecasts?

In a recent article in the Wall Street Journal, Pepsi Bottling Group Inc. announced an increase in profits, despite missing its forecast.

Over my career, I have created many revenue forecasts including when I worked at Western Digital and WebEx Communications. The last thing I would ever do is say that forecasts can’t be wrong.

But, are companies using the best tools and practices available to reduce the risk of a bad forecast? Most companies have not kept up with the changes in technology and still use Microsoft Excel to build complex volume, price and mix models based on complicated assumptions and variables to estimate a range of outcomes. Unfortunately, these models usually leave out the most important contributor to the amount of revenue to be generated — the “sales force” — and are usually out of date as soon as they are created.

Now, don’t get me wrong, it isn’t that businesspeople aren’t smart enough to realize that the sales team is extremely valuable due to their proximity to the ultimate customer. The problem is that the back end of the business doesn’t have a tool that gives them real-time visibility into customer demand.

That was until Right90 came up with an on-demand application that takes the input of each salesperson, by customer, by product, by geography and tracks the changes in volume, price and mix in real-time.

Imagine the potential savings: reduced inventory and obsolescence, improved on-time delivery and in-full delivery, reduced stock-outs and premium freight just to name a few.

Tags: , , ,
Posted in Sales Analytics, Sales Forecasting | No Comments »