This series of posts has followed what should seem like a logical progression: using the Lifetime Value figure; using components within that figure; comparing component values over time, across segments, and against forecasts; creating the forecast values with models; and using the models for simulation, planning, and optimization. But the discussion of forecasts can also lead in another direction, centered not on models but on building forecasts from the LTV components themselves. Since optimization is the ultimate goal of management, that should be the end of the discussion.

2007] will return lists of best books for each of the years from 2002 to 2007 (note the two periods between the two numbers). Even stock quotes (using the stock symbol) or a weather forecast regarding the next five days Subscribe via RSS Subscribe via Email

To do this, actual LTV figures must be compared with a forecast. The real question is where the forecast values will come from. It’s easy to calculate the expected LTV changes implicit in such a plan, since there is no program detail to worry about.) The trick, then, is to convert project plans into LTV forecasts. To build them into an LTV forecast, these Yesterday’s post discussed how values for LTV components can be compared across time and customer segments to generate insights into business performance. But even though such comparisons may uncover trends

Many published forecasts of technology adoption draw a line from current work as if the technology is determined to succeed (eg EDUCAUSE & New Media Consortium, 2006). Posted by Mark van Harmelen | 07 January, 2007 01:13 Thanks for dropping by, Mark. Posted by rlubensky | 07 January, 2007 10:11 eLearning & Deliberative Moments Transforming from elearning practitioner to academic researcher and deliberationist.

Postgraduate Medical Journal 2007;83:759-762 John Sandars June 10th, 2008 at 1:31 pm Medical uses for Web 2.0? The lesson is not that new technology should be ignored, but that it will take a while to establish the real long term benefit (that may not be what was originally forecast), with ups and downs on the way. Search the BMJ BMJ BMJ Journals BMJ Careers BMJ Learning BMJ Knowledge BMJ Group BMJ Group blogs Search BMJ blog: You dont need to be signed in

Although Pilot is a fairly generic “operational performance management” system (nothing wrong with that), both Decisioneering and TouchClarity involve some pretty sophisticated forecasting. This is good for customer experience management because CEM value calculations also rely on complicated forecasts. Managers’ willingness to employ systems that do such forecasts suggests they will To an industry analyst, one event is interesting, two are a coincidence, and three makes a trend. Last week has (at least) three acquisitions of analytics vendors: TouchClarity by Omniture

The study is designed to help online marketers set PPC and SEO budgets, forecast results, test online marketing programs, and even (toughest of all)—explain search marketing plans to your client or CEO. Among the key findings: Search marketing continues to grow at an incredible pace, with spending up 39% globally in 2007. A third of respondents anticipate double-digit spending increases on both SEO and Google PPC programs in 2008. Marketers rate SEO second and search engine marketing (PPC) ads third in terms of ROI, behind only house-list email marketing. MarketingSherpa recently published its Search Marketing Benchmark Guide for 2008 , providing data on cost per click (CPC), conversion rates, SEO and other key online marketing metrics.

Yesterday’s post on forecasting the values of LTV components may have been a little frightening. A LTV forecast requires managers to make that prediction or, perhaps, to rely on a company-wide policy so all such predictions are consistent. Because the LTV model will break out its projections by time period, it is possible to focus analysis on current year results (forecast vs. Most managers would have a hard time translating their conventional business plans into LTV terms. The connections between the two are simply not intuitive.

Before reading this book, I believed that economic forecasts were generally more reliable than weather forecasts (particularly living here in Minnesota, it's difficult to imagine anyone making a living being wrong more often than our weather forecasters). Throw in one large irrational economic act, or a hundred small ones, and economic forecasts become dreadfully wrong. Most business and marketing books are written for the broadest possible audience, have the tone of a motivational speaker, and offer specific, concrete guidance on topics such as drawing more web traffic, better organizing your time, or closing the big sale. The Black Swan: The Impact of the Highly Improbable , by Nassim Nicholas Taleb , is none of those things.

Customer value measures organize information in ways that reveal patterns and trends in customer behavior which traditional measures do not. - better forecasting. Forecasts based on individual customers or customer segments can be significantly more accurate than simple projections based on aggregate trends or percentage changes. Forecast quality has always been important but it’s even more of a hot button because I spoke earlier this week at the DAMA International Symposium and Wilshire Meta-Data Conference , which serves a primarily technical audience of data modelers and architects.