After nearly 10 years I generated the last run of data using DataPrizm. Last June, after one of our big clients did not renew I finally listed to Motoko and made the decision to wind down the tool after the last contract expires. I notified all of the remaining clients, some who had been users almost since the beginning, and after I made the last call it did feel like a massive weight had been lifted from my shoulders. I slept really well for the first night in a while. This feeling of relief was a big surprise to me as I had not relieved how much this tool was weighing me down emotionally.
As I pivot into the next phrase to focus on my original passion of developing market entry strategies, we have been making a lot of changes. The biggest of these steps has been shutting down a number of tools we have developed and it has been an interesting experience with a lot of self-realization. I new realize that I should have shut DataPrizm down years ago but there was always some interesting data mining project to come along that would extend its life. As recent as yesterday after reading this thought provoking article by Pete Blackshaw on understanding your “DataBase of Curiosityâ€ and what is required to own voice search and how DataPrizm already does help manage this complex process.
To Peteâ€™s point about understand the consumer through a Database of Curiosity we analyzed a years worth of site search and social media data for an amusement park and uncovered over 600k questions people had asked them with and estimated 15% of them being directly monitizeable. Unfortunately 60% of these questions did not return any result let alone one that could lead to a conversion.
Unfortunately, rather than looking at this as an opportunity, the client team was overwhelmed and wanted to ignore the data. After Senior Executive mandates they started creating content resulting in a 22% conversion rate and $6.8 million in revenue in just the first month. After this project we tried to get people to understand the gold mine in site search data and again tried when chat bots were all the rage and yet again to prioritize sequential queries for voice search but no one is really interested.
The next emotion to hit was frustration from my inability to find alternative solutions for clients. Even a few basic functions that match my workflow, that clients wanted to maintain were missing. One client evaluated 20+ tools and actually discussed buying DataPrizm since there was nothing on the market that could do half of what it could do.
The frustration was followed by resentment of the broader search community for many of these features not being required components in tools. I often asked myself, how could others not think this way and need the features of DataPrizm? When a reached out to vendors asking if functions were possible most responded that no one had asked for it or it should be possible in another tool. One provider told me in confidence, their main goal is to just aggregate as much data as possible and throw it into pretty reports no matter if it added value or not since that is what people wanted to pay for.
This blend of frustration and resentment caused me avoid the search community as everywhere I turned I heard nothing but gimmicks and shiny objects that did little to actually move the needle for a business. But what really turns me off are all of the people pontificating on the death of the keyword phrase and the rise of â€œSearcher Intentâ€ and â€œTopic Clustersâ€ yet no ways to manage or monitor their performance. There are over 100 articles on how you must move to topic clusters, capturing People Also Ask, and many of the anointed talking heads of Search all preaching about Searcher Intent but not giving examples of how to identify, segment it or even demonstrate business value. As a result of this frustration I stopped speaking at conferences and writing articles with the exception of PubCon.
The other application that is wrapping up is our Site Migration Tool. This is another one that I cannot understand why SEOâ€™s and Web Developers would not even try it. We looked at the root cause of migration and website integration failures and most were due to incorrect redirects of high value pages. This tool helped identify high value pages based on 20+ variables, and monitored them to ensure they were migrated correctly. In nearly every case, the potential users did not want to invest the time necessary to make the migration successful, yet said no decline in organic traffic was the main requirement of the migration. Ultimately we made significantly more money fixing failed migrations after the fact then we would have made preventing disasters. That is the sad truth of SEO – most only care when the traffic disappears.
Despite the numerous successes we achieved for clients, during the wind-down period I had all the feelings you would expect related to failure. Â I thought a lot about all of the things that I could have or should have done differently. Â So, following the brilliant suggestion from a recent LinkedIn post by Wil Reynolds on how Entrepreneurs learn – that more is learned from mistakes and failures and how you recovered then by all the warm fuzzy outcomes. That post made me change some of the things I started to write and capture some of the mistakes and what they taught me. Â In the spirit of helping, here are a few things that I think kept these doing from being widely adopted and a commercial success.
Despite creating nearly 100 tools over the year, one of my biggest realizations was that I am not wired for software development. Â I have been told I can be very stubborn, a perfectionist and a bit too laser focused when it comes to process. I did learn a lot about the software development process and am trying to turn these challenges into a strength to advise others on their software development programs. Â
It is very common in the search industry, you see features missing from tools so you code your own. Especially many old school SEO’s tend to be programmers and can easily code tools for themselves and then make the mistake of sharing them sometimes generating interest in them.
The challenge is converting these tools from functional tools that do what they are supposed to do to ones for paying customers takes on a life in itself. To get paying customers you have to make a lot of concessions to get them to pay for the service. Â
One of my problems, despite a very agile development methodology, I needed things to work correctly and I often delayed features since they could not accommodate all of the nuances that rumbled around in my head or the craziness of the various client sites and process. Â
My biggest mistake was not listening to my wife Motoko. She is my most trusted advisor and champion. Motoko is a realist and told me early on that most people will not want to do the work required to get maximum benefit from DataPrizm. She continued to remind me that most people only care about solving their individual KPIâ€™s and not solving all the problems of a company. She told me people never want to see a dashboard full of problems they know they donâ€™t have the resources to fix. She suggested that we no license the software but offer the output as a service and just provide reports and detailed action items.
We pitched this “research service” to senior executives and to Search Managers and did a number of pilot projects with nearly all of them loving the data but did not want to pay for insights they did not have the budget, time or support to implement. One prospect told me that if management saw this report they would be fired for incompetence when he saw the paid search cannibalization rate and the gap analysis of underperforming critical words.
Along the lines of not listening to Motoko, another mistake was not staying focused to the original goal of the application and focus on the consumer insights. It seemed the further away from pure data analysis we were, by adding customer requested functions, and those that matched enterprise search tools, the further we got away from our core audience.
Even with support from the C-Suite, amazement by business intelligence teams, and despite using all the right vocabulary we always ran into a roadblock when we had to explain how it worked. We tried every way possible to not mention “keywordsâ€ or â€œGoogleâ€ but you could see that was some of our imports. Once that was mentioned, we were immediately pawned off to the SEO team who viewed us as just another rank tool and we could never get back to the executive suite.
Another huge mistake was not taking investment money. Yes, we did get an initial grant from our first client but the rest of the development was funded by our consulting service and high margin projects that utilized the application. I believe had we taken an investment that would have given us runway to better market to senior leaders with actual â€œtake no prisonersâ€ sales people like other enterprise tools use. It would have also forced us to make the tool a lot sexier in terms of look reporting at the expense of function. Most importantly, It would have also forced us to end it much earlier when it did not grow fast enough.
Early on we talked to angel investors and various VC’s large and small and despite being profitable none would invest in it. In a couple cases the potential investors talked to Google who told them we violated their terms of service despite using two of their API’s as our primary data sources.
Another big challenge was not pissing off Google. Usually within a few days of winning a co-optimization project we would get a call from someone at Google threatening to take away our API license or removing me from the Technology Council. Ironically, in every co-optimization project, despite identifying how poorly managed the paid campaigns were and identifying hundreds of thousands of dollars in waste, every single project resulted in an increase in paid spend to capture the missed opportunity we identified.
First, while neither tool were huge commercial successes, I have to remind myself the applications were very successful/effective and I am very proud of them and the team that helped build them. They did more than pay for themselves and because of them, we won a number of consulting projects and international clients we have had for many years.
We estimate that DataPrizm created over $1.5 billion in value for clients. Yes that is Billion! One of our bigger projects identified nearly $400 million in new business opportunities and our first commercial project Avedenij $600k in paid spend in the first week and the modeling was expected to generate lifetime revenue value of over $290 million.
I am also extremely proud of what we did for Absolut Vodka. We mined millions of phrases globally to create the Drinks Discovery Journey that helped us understand the rich opportunity of targeting the Drink Curious and pulling them into the conversion funnel. Over the past 6 years this collaboration has resulted in 20+ Search, Digital and Data Mining awards including, to my knowledge, the only Search Marketing Data case, ever to be submitted for a Cannes Lion. I am extremely thankful to the team for all of the experiments we did using data and linking it to the entire customer journey.
You might ask if this is such a brilliant tool with these amazing case studies why are they being shut down. First and foremost I am tired of the stress of pushing the boulder uphill. I am pivoting into a higher level global strategy and don’t have time nor the desire any longer to manage these tools. We did reach out to a number of people to buy them over this period with some varied interest but because they did not have millions in revenue and/or did not match their tech stack we did not find a buyer.