Book – Big Data

big-data.pngThe book gives a good general introduction into the concept of Big Data and illustrates it with very interesting examples.  The examples show new business approaches possible in the world of Big Data, and how they are different from the traditional strategies.

Example 1: car manufacturer found an improperly working fuel gauge.  Normally, this discovery would start from a request to the gauge producer to solve the problem, what could affect new production.  car.pngHowever, the car manufacturer armed with data did not immediately informed the gauge producer, but created a software patch to correct the problem and sold it to the gauge producer.

Example 2: organizations use data tools to evaluate potential employees.  One organization discovered that graduates from top schools do not perform better than graduates from less prestigious educational institutions, as a result, personnel costs could be reduced with expectation of increased performance. In some jobs, employees with prior criminal record actually outperformed coworkers without criminal record.  Employees who used a non-standard browser were found more likely to take independent initiatives.

Important point: all data activities need to start from the strategy based on business need.  An average company won’t be able to understand and analyze all existing data – and it is not needed to run a successful business.  Only data relevant to a specific goal is required.

The SMART model — start with strategy, measure metrics and data, apply analytics, report results, transform your business — allows you to cut through the chaos.

Instead of starting with the data, start with your business objectives and what you are specifically trying to achieve. This will automatically point you towards questions that you need to answer, which will narrow data requirements into manageable areas.

Once you know what you are trying to achieve and you are clear on what SMART questions need to be answered, then work out how you can access that information so you can measure metrics and data.

The next step is to apply analytics, extracting useful insights from the data that can help you answer strategic questions. The data themselves are meaningless unless they help you to execute your strategy and improve performance.

Example 3: fashion retailer needed to understand who were its best customers.  The retailer measured foot traffic passing the store (mobile phone sensor), then combined this information with number of people coming into the store (the same sensor), and then combined it with actual purchases.  Result: window display could be optimized more effectively and one store was closed due to insufficient traffic.

Example 4: data aggregated from wearable devices became more valuable than the device itself.  Wearable device manufacturer re-imagined its business and became a data company, while still producing the device to collect data.

Important point: analytics and data visualization are part of the same task.  An idea that first data is analyzed, then visualized by a different group, and then presented to a decision maker would not provide the most value.  Analysis and visualization need to work together and the decision maker should not be expected to use visualization tools – but to receive the insight generated by the analysis.

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An interesting site mentioned in the book Information is Beautiful – the site highlights data visualization examples.

Love Your Pipeline Event

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Interesting: a panel of advanced industry marketers did not have significant experience in every industry area.  In many cases, they were starting ABM and just moving into predictive.  The shiniest of the shiny industry objects are new, and every marketing organization is trying to understand how to use the new opportunities for their business, or if these opportunities are beneficial for their business.

It was a pleasure to see “people” and “process” taking central stage in transformations of marketing organizations, and tools moving to the supporting side, where they belong 🙂

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Predictive technologies discussion:

“If you don’t have right people, don’t just buy the tools.  You won’t get enough benefit out of the tools…”

Another interesting perspective: improvement takes time…  and, in many cases, years.  (Very good visualization of “marketing improvement” from Insight)

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Predictive technologies discussion:

“We just put some tools in.  In 6 months we will see data…”

Surprising: sales aspect of ABM may not be entirely positive. Sales might feel threatened by account-based approach: “Now they will give me a limited number of accounts and I can not go outside the list…”

Tools mentioned during the event:

Gecoboard  – a tool that allows visualizing and easily exposing data in the office.  The board integrates with a variety of apps (including SFDC, Google Analytics, Excel).

“Live TV Dashboard that improves key business metrics”

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Conversica – a “sales assistant” AI, that communicates with prospects, warms leads and sends them to sales.

The tool has a free trial limited to 25 leads, and not limited in time…

Book – The Business Blockchain

blockchain.pngThe book is a good introduction to the industry concepts.  Blockchain often perceived as a technology only without understanding of other aspects of the phenomenon.

What is the blockchain?

Blockchains are new technology layers that rewire the Internet and threaten to side-step older legacy constructs and centrally served businesses. At its core, a blockchain injects trust into the network, cutting off some intermediaries from serving that function and creatively disrupting how they operate.

Blockchain has technology, business, and legal aspect.  It is a mechanism that allows transactions (and currencies) without a need of trusted central authority.

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As blockchain has three different aspects, it can potentially bring change and innovation into technology, business, and also legal establishment.  From another side, blockchain faces obstacles to overcome in all three fields (technical nuances, business processes, and local laws).  It will be interesting to watch how blockchain will evolve and which aspects will be more critical for wider adoption.

Book – Matchmakers: The New Economics of Multisided Platforms

matchmakers.pngYes, it is completely correct, I do not remember anything related to multi-sided platforms in business school curriculum several years ago. The book gives an excellent explanation of business principles behind the phenomenon.

Though the discussion of platform externalities and negative network effects are common, and the Book – Platform Revolution has an Glossary.pngexcellent description, Matchmakers emphasizes the economic reality of the multi-sided platforms.  Book site includes a convenient glossary of industry terms.

Interesting: a platform relaying on advertisement has three sides (producers, consumers, and advertisers).  Microsoft Windows is a platform that connects computer manufacturers, app creators, and app users, where Microsoft Office is the most popular apps.  However, when Microsoft tried to find producers for X-box console, the search was unsuccessful as game console is the subsidy side of the platform.

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opentable.PNGOpen Table initially tried to attract “eye balls” in general, and the strategy was unsuccessful.  The company signed up many restaurants, but not enough in one market to become attractive for the consumer.  The company changed its approach, and concentrated on specific markets to generate enough restaurants to be attractive for the potential diners.

BrightCove was conceived as a platform, but the approach was unsuccessful, and company changed its strategy.

Multi-sided platforms need to be designed to encourage participation from different sides.  For example, money-sending platform would charge more for sending money to a person who was not signed up than to a person who was signed up.

BMA -Customer Relationship Intelligence (CRI)

CRI.pngCustomer Relationship Intelligence (CRI) – an approach that is trying to quantify and organize customer understanding and apply this understanding to improve business results.  The objective is to build customer insight into product/process/service improvement (avoid pursuing improvements that won’t be valuable for customers, and concentrate on the improvements that will be).

“Your most valuable customers want a relationship with you…”  because it matters for their company (their infrastructure, etc.)

CMO Council observed a shift from traditional metrics to “customer efforts” measurement.  (Customer efforts include all activities related to purchasing, installing and using the product).

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Example: a group of engineers went to the customer location to install the product.  After they came back, a new requirement was added for product development to limit time required for installation.

“Customers do not quite care about being delighted,”  but they will be happy if you “fix staff” and it will be easier to work with you.

book.PNGBook to read – Customer Relationship Intelligence: A Breakthrough Way to Measure and Manage Sales and Marketing

“Customer Relationship Intelligence brings a fresh, new perspective to sales and marketing: new relationship metrics and a breakthrough, actionable framework for real-time management, tied to profit. It is about teamwork and collaboration. It is about executing strategy. It is about retention, profit, and competitive advantage.

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84 Percent of Website Visitors Convert on the First Visit

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Yes!!  The data is available, thanks to https://www.straightnorth.com/  and my “confirmation bias” could not be happier 🙂

No, we do not need to wait until the initial site visitor will become “ready,” site visitors are ready now, and we, marketers, should take advantage of it.  The report 10 Ways to Make Your Lead Generation Website Convert on the First Visit gives recommendations on how to use the data to benefit our companies.

Lead generation websites: If you do not make a great first impression, you will not earn a conversion, and you will only get a second chance 15 percent of the time.

Companies that want a full pipeline of sales leads MUST pull all the stops and make their websites first visit conversion machines.

Another popular confusion is clarified in the report:  only 23 percent of online leads come from mobile devices (data source is 70 percent B2B and 30 percent B2C).

Based on my personal experience in B2B and B2C, the number is even too high for B2B; the 30% of B2C in the sample might have received the most of mobile attention.  Each company should probably evaluate its own opportunity cost of heavy investment in mobile.

Yes, we, B2B marketers, can be in love with our long sales cycle and sophisticated buyers journeys, but we can increase effectiveness of our marketing efforts if we think about first visit more.  And – there is nothing to loose 🙂

HBR’s 10 Must Reads 2017

hbr.pngThe excellent collection overall has two interesting articles related to automation of knowledge work.  One of the articles emphasizes imperfections of algorithms and another one suggests strategies for humans when AI is able to perform some of their tasks.

(Based on my experience in marketing, marketing automation did not reduce the demand for knowledge workers, but rather expanded the “knowledge” required to be a marketer.  Though companies thought initially that they will need less marketers with the advent of automation, this assumption was incorrect.  The automation allowed more knowledgeable people to achieve better results, but the minimum number of people  and minimum amount of knowledge to realize any ROI on automation actually increased.)

Algorithms Need Managers, Too

The article suggests that however sophisticated, algorithms are literal, require very precise instruction and understanding of their limitations.  The typical example is giving the instruction to an AI to “save the Earth,” which will proceed with an attempt on elimination of humans as the most reasonable method of achieving the objective.

Example: a predictive algorithm was selecting products that can be purchased in China and re-sold in US.  The program worked well until customers started to return the products.  Long-term product satisfaction was not built into the process.

Example: algorithm can predict clicking on an add, but the required result is a sale; optimization on the click will generate more activity, but may not generate revenue.

Example: Netflix predictive algorithm for DVD rentals did not apply to video streaming.

Also remember that correlation still doesn’t mean causation. Suppose that an algorithm predicts that short tweets will get re-tweeted more often than longer ones.  This does not in any way suggests that you should shorten your tweets.  This is a prediction, not advice.  It works as a prediction because there are many other factors that correlate with sort tweets that make them effective.  This is also why it fails as advice: shortening your tweets will not necessarily change those other factors.

Beyond Automation

The article ponders the future of AI replacing some of the knowledge worker’s tasks, and what knowledge workers could do:

  • Step up (strategy)
  • Step aside (area that requires human interaction)
  • Step in (work with algorithms – what might be a default “augmentation” approach)
  • Step narrowly (area within profession that is unlikely to be automated)
  • Step forward (create next generation of AI)

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How Indra Nooyi Turned Design Thinking into Strategy (Pepsi)

The article explains very well “design thinking” on easily understandable examples of Pepsi.

Interesting: Pepsi also uses a variation of “reverse innovation” – launching a product in smaller market (outside of its home US market), where cost of failure is acceptable.

Interesting: Pepsi calls healthy products “good for you,” and products that do not fall into this category “fun for you.”

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Every morning you’ve got to wake up with a healthy fear that the wold is changing, and a convection that, to win, you have to change faster and be more agile than anyone else.

People Before Strategy

Discussion of people should come before discussion of strategy.  What are employees’ capabilities, what help might they need, and are they the very best?

Interesting… 🙂