How To Turnaround A Failing Business

If you run your own business and it has all been plain sailing, then you are one of the lucky few. Most businesses have their peaks and troughs, and most must fight their way out of a crisis or two. The types of businesses most at risk of experiencing a crisis are the small startup businesses. When you have only a few customers, for example, one large customer failing to pay can put the business at risk.

They say that setbacks are a great learning experience. But that’s small comfort when you are looking at the potential demise of your business. So, here are ten practical tips for any business owner who is struggling with a business in crisis.

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Goal Setting: The Truth About Change

Every executive I’ve ever met seems to love change programs. They’re excellent fodder for any update you need to send up the line. And in some cases, they might even generate actual results…

That being said, organizations appear to systematically misjudge the time and effort required to deliver these beasts. Various statistics from leading consulting firms point to a high failure rate for corporate strategic initiatives. Perhaps some of these might have been salvageable with different assumptions.

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Getting Run Over By a “Paid For” Truck

I stared at the email and fumed. What do you mean we lost the business?

This wasn’t driven by pride or ego. We HAD given them our best price, quoting a pricing level where we should have been assured of victory. This proposal was at break-even pricing to protect other business in the account. On a generic spec, commodity grade product. Where we had the lowest cost in the wholesale industry. (I was very sure of that, since I had personally negotiated that deal).

And we had just… been owned. Not merely losing the business, but getting buried.

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Applying Quality Principles To Contract Negotiations

Most of the business community regards strategic partnership negotiations as unique events, immune to process improvement analysis and principles. There’s a certain logic to this: there are unique business needs behind each proposed partnership and each potential counter-party has their own considerations.

However, if we take a step backwards, we can generalize the situation into a process of sorts. Each party has a set of interests they want in the contract terms. Some of these are easily reconciled, in a clear zone of “reasonable compromise” which a reasonable person would agreed upon. Others require a some level of haggling and compromise. We can rate the “quality” of a negotiation outcome based on how close the team is able to get to a set of “target” terms. Another quality metric worth tracking is the percentage of deals which utterly collapse in the negotiation process, with one or both parties unable to reach a deal.

Along the same lines, we can establish some metrics for the “efficiency” of the negotiation process. This is particularly important for small and medium sized deals – while we may be able to squeeze additional concessions from these relationships, it may not be worth the incremental time required. You can track the total time spent on a deal’s paperwork, either as clock time or by tracking the number of revisions your attorney needs to review.

The combination of these perspectives yields a process measurement model for contract negotiation. We can measure quality by tracking % of deals closed and % of deals meeting target terms. Process efficiency can be tracked using clock time or legal revision cycles. This gives us a way to start comparing the impact of different approaches to managing the process.

This exercise gets particular interesting when you need to negotiate the same deal multiple times. Your organization probably does this every day within a specific customer segment – walking into similar accounts with the same core program and discussing the same key points. The data from this effort can easily start to add up, especially if you can have the team keep careful notes and do a little split testing.

Some things worth looking at:

  • Negotiation outcomes will vary significantly by lead negotiator and support team. Don’t neglect differences in team size and support roles – strategic negotiations are very much a team sport.
  • The process of how you get interests on the table for discussion. While it may be a time-honored tradition to just chuck your standard agreement at the other team and ask them to red-line it, you may want to consider a more collaborative approach such as white-boarding a joint terms sheet.
  • Organizational rank and “clout” of counter-party leadership. Some of the most effective sessions I’ve seen were “princes against kings” where you had mid-level executives negotiating with the partner’s senior management. There was ample room to reflect and adjust direction as needed.
  • Effectiveness of point-by-point negotiation vs. trading “packages” to resolve differences
  • Ability to control final concessions and “one more thing” negotiators (the Columbo crowd)
  • Negotiator experience also plays a huge role, particularly with that specific type of deal. They will get more confident and effective at addressing questions as they learn the nuances of your offers.


Strategic Flaws of Statistical Price Targets

These days, everyone in the analytics space has a pricing product to talk about. Toss enough transaction data points at the problem, you’re going to get a few insights that may help you earn higher margins. Even better, moving the pricing process to a modern software tool often improves your control over pricing errors and sales rep concessions.

Can this work? Absolutely. For an organization with weak pricing practices, the first wave of pricing optimization often yields brilliant results.  For a typical manufacturer or wholesale distributor, this can raise your net profit margin by one to three percentage points. Given the low margins in many of these industries, this is a meaningful improvement in the overall profitability (and thus, valuation) of the business.

But you need to have a careful plan for Act II. There are limits to how far you want to ride this horse.

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Investing In Existing Websites: Growth and Failure Rates Three Years After The Auction

Several years ago, we completed a website revenue study focusing on small websites. The goal of this study was to understand how much a typical site should make. We accomplished this by looking at 100 websites listed for public auction at a major site. We recently ran an update on this study to understand how the sites were doing today.

This update measured two things:

  • What % of the sites were still online and indexed by Google?
  • Has the site gotten better or worse since the sale (using competitor analysis tools)?

Of our original 100 sites, 93 were in a position to be effectively tracked using 3rd party competitive research tools such as SEMrush. The remaining sites got a hall pass; these had been executed as confidential sales (no site name), where we vetted the site anonymously and were unable to find them for a follow-up. Since a basic due diligence review was run on every site included in the study, we are comfortable that each of the sites was operating as a legitimate business at the time of the sale – although many were “window dressing” from a SEO and traffic generation perspective to boost their statistics.

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Accelerating Analytics: Decrease The Cost of Asking Useful Questions

After twenty years in the business, I am giving up on the idea of asking brilliant questions. They don’t exist. Ironically, most of the questions which have delivered serious money in the past tended to look like relatively dumb ones…

The first set of significant wins I had in my analytics career was in direct marketing, where I moved the campaign analysis process for a $5MM/year program in-house. From a technical perspective, this was pretty straightforward: write a SAS program to merge our mailing list with our customer file then aggregate response and sales data. Since a common key existed on both files (finders file number), it was a simple matter to join the files and summarize the data into an Excel Pivot table. Intern level stuff.

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Linkedin Endorsements: How Might They Affect Linkedin’s Search Algorithm?

Linkedin rolled out their one-click endorsement feature this past month. As I’ve traded clicks with friends and colleagues, I’ve been trying to figure out what their real goal is. On the surface, this feature feels redundant with their existing “recommendation” feature. Given their aggressive efforts to promote this new feature, the data they are gathering is clearly important to the development of the algorithms behind their services – but how?

Linkedin has been fairly quiet about the inner workings of their search engine. This is likely to prevent people from manipulating the results, since there is significant value in being on the first page of a Linkedin search for a lucrative professional skill. They share some basic pointers about how to “be visible” on their help page. Key points from their page:

  • There is no single rank for Linkedin Search – results are unique to each user/query
  • The profile keywords of both parties (searcher, results) play a significant role
  • Rankings are adjusted based on how prior searchers have reacted to your profile

While the above metrics are fine for identifying which candidates are relevant to a search, they don’t rate candidate quality: who actually knows their stuff? What’s missing here is a broader assessment of “page trust” (graph model analysis concept) that candidates possess the skills that they reference on a profile. For example, Google’s search algorithm incorporates an evaluation of the credibility of a site using link patterns, brand signals, and social activity. With these new features, it looks like Linkedin may be trying to adapt Google’s Pagerank algorithm (or something similar) to ranking candidates for specific skillsets.

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Escaping The Walled Garden of Enterprise Analytics: Using R and Python For Data Analysis

In which an experienced analytics guy advises the younger generation to leave the walled garden of enterprise analytics tools and learn how to write code using a real programming language. Specifically advocating the use of R and Python for data analysis and related programming. But hey, I’m flexible on that point…

The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offense.

– Dijkstra

I was taught a long time ago in some Management 101 course to sandwich constructive criticism between two compliments. So I’ll open with this statement:

SAS and the other BI vendors have done a nice job of bringing statistical computing techniques within the reach of the typical college graduate.

Now pull up a chair and grab yourself some popcorn, since I’m going to bite the hand that fed me for the first half of my career. I spent the first seven years of my career in roles involving significant usage of SAS and a variety of drag & drop query tools. The COBOL of the analytics world.

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The Online Marketing Sanity Test: Is Your Website A Waste of Money?

‘Cause only one thing counts in this world: get them to sign on the line which is dotted.

– Glengarry Glen Ross

Here’s one simple question that will identify about 85% of the completely worthless website and content development efforts out there:

Does Google show your site to people who don’t already know your name?

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