Key Performance Indicators (KPI) are a way to provide real-time measurement of performance in fulling strategic goals and objectives. They are a perfect starting point for any discussion about digital transformation because they cut to the heart of the strategic initiative and they provide a way to dynamically monitor the effectiveness of the strategy.
We like to focus on leading indicators where we are able to measure an activity that leads to a desired outcome; for example: “If I do activity A well, then I will get result B”. The activity directly produces the KPI. The indicator must be demonstrable, relevant and easy to measure; the simpler, the better to measure performance and ensure success.
We use classic Business Architecture to connect existing strategic objectives to initiatives, and then assess the initiative for its ability to deliver the objective required for the transformation. We are also able to pinpoint what capabilities are missing, where to add them and what we’d like the outcome to be.
For example, we want to enable “buy online, return to store” as a strategic course of action to produce the objective of increasing appeal among a new consumer segment. The strategy requires that we connect online ecommerce systems to the in-store transactional systems. What capabilities currently exist, and how might an initiative augmented or add to them in order to fulfill the objective? The KPI will measure the appeal among the new consumer segment.
Our Business models and standards library contains over 150 KPIs covering a broad range of topics. These help to expedite development and serve to ensure we don’t miss the obvious.
Further, by breaking down a complex problem into smaller, well documented and measured tasks we can see performance issues and understand whether we are on track toward our strategic objective well in advance of serious failure.
Well-designed KPIs serve as vital navigational instruments, giving a clear picture of current levels of performance and whether the business is where it needs to be before making huge commitments on a finished system.
The KPIs fit into every stage of our practice in aid of your digital transformation objectives.
Continue the conversation with me. Let’s go do it.
As a marketing strategist and practitioner of business architecture, I believe Michael Porter’s three generic strategies of cost leadership, differentiation, or focus are as important now as when first promoted in 1980.
In practice, these have stood the test of time and practice though they have not recognized the birth of new generic digital strategies such as scale, network effect, and brand leadership that have recently proven successful.
In particular, the network effect enabled by the internet changes the matrix as Strategic Advantage would recognize “Free” as independent of Low Cost Position, and the Strategic Target would reflect a universal or omnichannel approach rather than Industrywide or Particular Segment.
Beyond cost leadership, differentiation, and focus, I would like you to consider that scale, network effect, and brand leadership are effective generic strategies for building unique and durable businesses.
Historically scale and size were tied to cost leadership where volume was linked to cost and pricing of the components; the more resources you bought, the cheaper the finished goods. The ability to scale audience was not an option given the high cost and limited reach of available media.
Today, scale as a generic strategy is highly visible on the Internet via socially-oriented site including Facebook, Snapchat, eBay, and OKCupid. The component resources and provision of the product is immaterial because the product are the customers. Or as Andrew Lewis said: ‘If you are not paying for it, you’re not the customer; you’re the product being sold.’
Only with scale via a huge user base do these businesses succeed. Ultimately these businesses prosper with revenue from data about users, advertising, transactions, and subscription fees. GOBOSH — Go Big Or Stay Home.
Similar to scale is the network effect (aka, network externality or demand-side economies of scale); the effect that one user of a good or service has on the value of that product to other people. This business case is typified by phone, chat, financial exchanges, most apps (enhances a smartphone) and browser plug-ins. Unlike scale, a network can be small and still be successful as demonstrated with chat software like WeChat and Skype or gaming sites and software from xBox or Steam.
Newer examples come from Internet of Things (IoT) devices and networked sensors to capture and process data. This as a generic strategy depends upon knowledge, insights, and visibility to emergent behaviors; i.e., signals that the engine or pump is about to break.
Again, one might argue that to achieve cost leadership, differentiation, or focus one might include the network effect as a subset or component. To test this, consider the inverse: if you single mindedly pursued the network effect, would cost leadership, differentiation, or focus follow and be subordinate?
The final generic strategy to consider is branding. Clearly this is questionable in many regards as it is an intangible concept in both economic value (intangible asset) and physical substance (conceptual). Regardless, you see dominant brands such as Red Bull and Beats that prosper without cost leadership, differentiation, or focus on products or services. These brands assert a position of fame, affinity (badges), and experiences (club); there is little product differentiation, no cost benefit and high competitive substitution.
Suspicion of branding as a generic strategy most likely relates to the fact it is difficult or confusing and lacks precise models or processes.
Expressed as “the fame game” or a “celebutante” we need only look to the new breed of celebrity including Paris Hilton and Kim Kardashian who are famous for being famous. From this perch numerous business opportunities have emerged as their branding is worth millions. This is clearly a generic strategy that does not fit the Porter model.
For new and existing companies, these new generic strategies can be uses as guidance for digital transformation or as a way to pivot into new markets. What would your business look like and how would you operate if a billion people were engaged in some way like they are with Facebook, Google, and WeChat?
Is there value in creating one or more networks among employees, vendors, or customers? Could you apply network tools and sensors to your business and create new capabilities and value?
Is there value in your brand and can it be elevated to a point of dominance like Apple or Nike?
Continue the conversation with me. Let’s go do it.
I love that people are focusing attention on “digital transformation.” Every consulting firm has a practice area, every research firm and think tank has a report and every business magazine has a series of stories. Google shows 7.3 million search results.
My entire career has been about transformation. In the early years it took the form of new product development and generating demand with advertising. The transformation was largely formed with persuasion and clever ideas, communicated to people with TV and print, and by making the item available at the local market.
By the 1990’s the transformation started becoming digital with infrastructure things like websites, email, and ecommerce — requirements easily added to any legacy business. This was largely a tactical exercise with marginal impact to the core of a business. Of course many new businesses were created, born of the digital ecosystem and a growing generation of natives.
Having celebrated the 25th anniversary of the World Wide Web, we now see a type and magnitude of new company not previously possible. Facebook, Alibaba, Uber and Airbnb are examples for this new type of company whose existence is grounded in digital technologies and which are highly differentiated from their competition.
Interesting, but what about legacy companies — companies that make and sell things the good old fashion way? Theodore Levitt’s famous marketing myopia mantra – What business are you in? – has little value in the face of this type of competition.
What is needed is a way to think about an existing business in a different way. Just like a building is subject to architecture for the planning, designing, and constructing, so too is a business, especially a business that seeks to transform itself.
With this single blueprint of the business structure, all stakeholders can share an understanding of interconnectedness and how changes in one area will impact other areas. What’s more, this blueprint will provide common vocabulary and a framework with which to discuss transformation. Everyone gets on the same page and shares the same knowledge.
In a building renovation, professionals often refer to the “bones” of a building, the equivalent to our bones and skeleton, being good or bad. Business bones are also a good metaphor for what is good or bad as a subject of transformation.
In a taxi company example (aka, Uber), one bone is a hard assets/cars and another is labor/driver. The taxi company, wanting to remove these bones would need to understand the changes for hiring/re-staffing and vehicle provisioning.
In a general merchandise store example (aka, Alibaba), the marketing and delivery bones would need to be added and the store either discarded or transformed into a warehouse. Staff would be retrained to new positions and hours of operation.
In the context of digital transformation, the critical component involves Information Technology Transformation (ITT). Business Architecture is particularly well suited to mapping and aligning transformations from Business to IT and the current “as-is” to the future “to-be” requirements.
With a clear understanding of the business structure and bones, plus how to align Business and IT transformation, innovation has a solid bedrock from which to build.
Digital transformation is a hot topic in Corporate America for good reason: legacy companies, selling “real” products with established value and customers nurtured over many years, are getting crushed by new companies who seemingly have no value other than some digital-internet-mobile-social voodoo.
You’ve heard about Uber and Airbnb, but recently the Dollar Shave Club ($SC) — selling razors in a field dominated by two Fortune 50 companies — was purchased by Unilever for $1 billion.
$SC was notable for having a great sales video and direct-to-consumer relationships but it neither made its products or mass promoted nor distributed them; a perfect disruption in the mass distribution channels.
The lesson is not in the disruption, but rather how it is possible to reinvent, innovate, and leverage up existing assets.
My friend Rob called bullshit on me regarding “how easy” is was to digitally transform a legacy company into a modern day Uber or Airbnb of a given category.
Since both Rob and I use Business Architecture as a way to describe value creation in any given company, it was fairly easy to explain how one might transform a traditional company with some applied digital tools and techniques.
Rob knows I dislike dogs (cat guy), so he said I was CEO and asked how I would digitally transform a dog food manufacturer. Let’s assume this is a $1B revenue business, operating for 50+ years, has a strong regional brand with loyal customers, strong distribution to specialty pet stores, food, drug, and mass merchants, and sells 10 SKUs of wet and dry dog food.
A business architectural analysis of the potential for Digital Transformation highlights 4 value streams that support the opportunity:
Product Manufacturing, with both the recipes and the branding. Labels alternative branding are inexpensive,
R&D knowledge about dogs, their diet, nutrition, and overall health
Strong just in time operations and logistic to service over 2,000 accounts
Basic ERP systems, accounting and general technology systems integrated across most of the Operations, though lacking for sales, marketing, and customer relationships.
To undertake a digital transformation, key missing components would have to be addressed:
A value stream that dealt with the customer or “end-user” as a stakeholder. Having relied upon a traditional distribution and direct retail relationship, there was no knowledge about or engagement with the actual customer: the person, family, or dog.
Further, there are no capabilities or information in support of consumption or purchase behavior. There was nothing about the dog’s health, activity, age, location or its’ well being. For example: was the dog on a farm or ranch or in a city apartment?
Starting with low-hanging fruit — things other companies are doing and 3rd party suppliers offer — we came up with these ideas that address both the relationships with consumers and the underlying economic model. These ideas are easily applied using existing assets and value streams and minimal investments for pilots and test markets.
License an activity tracker digital device (e.g., Fitbit, Garmin) or for dogs. This will include a smartphone and computer app tailored and branded to the company specifications. Critical to this is an understanding of diet, activity, and nutrition. For example, the dog may be more active in the summer and need more protein, but less active in the winter and need fewer calories with more vitamins and other nutrients.
Create a CRM infrastructure and outreach program to individuals; this includes customer accounts and billing, customer service, and related logistics capabilities. The CRM should also be tied to advertising and other outreach programs.
Develop and promote a custom direct-to-consumer distribution program that automatically sends “customized” food. A TBD big issue is the ability to change formula and recipe to meet certain requirements for calories and quantity. Ideally, Fido would have his own label with portion control.
Disruption to existing channels of distribution can be offset with data and knowledge about customers and purchase behavior, plus separation with new branding and direct to consumer (and dog) relationships.
This is certainly an oversimplification and abbreviated description of both Business Architecture and Digital Transformation. It does however exemplify the clarity of thinking and ability to focus strategy into a few meaningful and actionable activities.
Most people think Artificial Intelligence (AI) looks like a robot, is smarter than a human, and only speaks the truth.
The entertainment industry would have us believe that AI systems are attractive Caucasian robots with minds of their own like Ava in Ex Machina and Sonny in I, Robot. And that they come in angry versions like the Terminator soldiers and shiny happy servants like C3PO in Star Wars.
With this connection to humanity, it’s funny that nobody talks about stupid AI.
Be certain, we’re going to have a world of stupid AI, some might be robotic, but regardless of form, their knowledge base will be tilted, biased or just plain wacked. Just like people.
Here’s how it goes down.
In the beginning there will be an AI platform, IBM Watson is a good example of a robust AI you can buy today. Off the shelf Watson knows nothing, not even math, and does nothing except learn. You need to teach it. You teach it by feeding it information.
What information?This is where stupid comes in.
Information, knowledge, and wisdom is very confusing when you try to add any amount of accuracy and reliability; it is downright head breaking when you try to subject it to truth. These are age-old philosophical issues dating to Aristotle, Plato, and Confucius and we still haven’t figured it out.
Many people believe a popular opinion is true, or if it comes from a certain source, it must be accurate. With some social issues, like Celebrity gossip, popularity does rule and proofs are difficult to come by. It gets harder when you contrast, for example, creationism vs. evolution in a literal interpretation vs. science discovery argument.
Let’s feed the AI with the body of knowledge (a corpus of information) built from the billions of words written in favor of creationism. Let’s also add billions of words that align with it and do not contradict it such as Evangelical teachings.
Then start asking this Cognitive Creationist AI some questions.
In the example case of using IBM Watson as the platform, you will get a “probabilistic” answer with 90% confidence that the world was created between 5,700 and 10,000 years ago. Part of the rationale supporting this is that according to Gallup survey’s, 47% of USA adults answered that “God created humans in their present form at one time within the last 10,000 years.”
Not to pick on religion as an easy target, but similar issues are pervasive in “sciences” particularly medicine and nutrition. The science around sugar and high fructose corn syrup has raged on for decades with study contradicting study. Same for medical issues around cholesterol and high blood pressure, their causes and treatment, using diet vs. pharmaceuticals are still subject to divergent facts.
Also in terms of medicine, countries around the world differ on what best practice treatment might be recommended, such as herbal over drugs, or acupuncture over surgery, so a medical AI in China could yield different answers that may or not be stupid.
While not technically stupid, note an AI could be trained with structured information to return biased answers; for example as a new form of paid endorsement and Branding awareness. The answer to “the best, most popular, favorite, cool, great” thing can be taught and also associated with certain other answers and attributes.
AI, where is a popular beach destination in January?
Answer: The Cancun-brand resort is popular and has a special offer that includes Airline-brand discounts and Branded meal vouchers.
AI, I need more calcium in my diet.
Answer: milk is popular but Brand name cheese is more tasty.
With apologies to Forrest Gump, “stupid is what stupid teaches.” Let’s hope we humans are able to ultimately find Intelligence of the non-artificial kind first.
Or maybe my AI will negotiate with your AI for a true fact.
Advertising is my friend. I started my career as a MadMan working for Della Femina Travisano and Partners at 625 Madison Avenue, and went on to found US Interactive a digital media and e-commerce development agency.
Only now has it dawned on me that the problem with media & advertising is you don’t know what you’re buying, and you’re never sure you got it.
The Internet was supposed to change that, but instead, it made it worse.
From the earliest days of magazines and newspapers, you knew the ad size and circulation, and even some distribution and location information, but you never knew who, where and when your ad was being consumed and if it did anything.
Radio and Television changed some of that by improving upon where (home) and when (schedule), but gave up who and circulation (called reach), which created the problem of unknown frequency (how often a person viewed the ad).
Digital media and the Internet was supposed to change all that, and it did, but not for the better. We were supposed to know the exact time and place, we would know how to attribute and track actions, and we could predict preferences by observing actions. Accountability and targeting were the promise.
Unfortunately thepromise has failed, and the solutions are elusive, though we can boil it down to 3 issues and offer 3 solutions.
The first issue is all about fraud. Server farms and botnets all around the world click on ads into and out of fabricated content sites and pages. Estimates suggest 92% of ads served are not seen, thereby defrauding advertisers of $7.5 billion!
The second issue of buying involves “programmatic” which allows automated systems to bid/ask, then automatically purchase and serve an ad that fits within programmed parameters. While this simplifies buying, it exemplifies the fact you do not know what you are buying, and obfuscates what you actually got; in Knowledge Management and Marketing Theory, this would be characterized as buying an abstraction rather than a specific thing.
The third issue is target market “inference” or the belief you can profile a browser and cookie into a prospect customer because of behavior, context, or availability. We all know how this works: you constantly see ads for products you’ve already bought. I liken it to driving forward by looking through the rearview mirror to chase after a customer that waved at you as you’ve long since passed.
So much for the tragedy, what are the solutions to knowing what you bought and knowing what you got?
The first and easiest solution is to put the people back into the equation. The human factor is more expensive, you pay a premium, and for the most part, can only buy from “premium publishers” who still have a sales staff to package a plan and take your money. Smaller niche publishers can be contacted directly and you should buy a longer-term sponsorship. It might look like you are spending more money for less, but in an industry where 92% of ads might not be seen, the premium of buying real ads to reach real people delivered by real publishers is economically smarter.
The second solution is to become a publisher yourself, using the same tools for creating fresh, original content (articles and video), building audience (e-mail programs), and curating content (save and republish). This is a long-term strategy, it is durable and sustainable, and it enables both brand building and immediate e-commerce opportunities especially when linked to a mobile strategy and apps.
For large advertisers that need scale and efficiencies from automation, the third solution is to abandon most of the ad-tech, which accounts for about one-third of the costs, and channel those funds back into buying “tonnage.” Acknowledge the fraud and waste, but make it up in volume and run big data analytics instead.
Of course mileage may vary for you and your business, but the reality is the Internet environment and technology is not making the digital media and advertising world better.
Accountability is elusive and requires personal hands-on management to succeed. CMD-Y: History.
I just discovered some absolutely amazing maps of Manhattan created from a mash-up of 4 David Rumsey maps from the 1800’s on top of the current Google Street map.
I discovered a few very interesting things. Note the build out of the island at:
World Financial Center @ Rector St,
Chelsea from West 20-36 St, and
UWS around 70th St.
But the most awesome change is at the top of the island where Spuyten Duyvel Creek has changed shapes when they filled the creek and made Marble Hill part of the Bronx and CONUS; toggle the 1836 map and 2012 maps to see the dramatic change. Also, to that Marble Hill remains part of Manhattan even though it is now in the Bronx.
Also, the other 2 islands that are part of Manhattan also had big changes: Governors Island more than doubled in size, and Randalls Island was combined with Wards Island and Sunken Meadow Island to also double in size. This was a great idea since both are largely recreational.
Play for your self here: http://maps.google.com/gallery/details?id=zDoXIAU5tGiw.k8dc28wqOJhM&hl=en