Viewing offline content

Limited functionality available

Dismiss
Deloitte Middle East
Annotations
  • Services

    What's New

    • Deloitte175

      Join us for a celebration of 175 years of making an impact that matters.

    • Building the Resilient Organization

      2021 Deloitte Global resilience report

    • 2020 Global Gender Impact Report

      A collection of Butterfly Effect stories highlighting how our Deloitte professionals are positively impacting the lives of women and girls around the world

    • Audit & Assurance

      • Assurance
    • Consulting

      • Strategy, Analytics and M&A
      • Customer and Marketing
      • Business Operations
      • Human Capital
      • Enterprise Technology & Performance
    • Financial Advisory

      • Mergers & Acquisitions
      • Forensic
      • Real Estate
      • Turnaround & Restructuring
    • Risk Advisory

      • Strategic & Reputation Risk
      • Regulatory Risk
      • Financial Risk
      • Operational Risk
      • Cyber Risk
    • Tax

      • Global Business Tax Services
      • Indirect Tax
      • Global Employer Services
    • Deloitte Private

      • Family Enterprise
    • Legal

    • Sustainability

  • Industries

    What's New

    • Deloitte perspectives

      Leadership perspectives from across the globe.

    • Deloitte Insights App

      Our thought leadership and Dow Jones news, now at your fingertips

    • Future of Mobility

      Learn how this new reality is coming together and what it will mean for you and your industry.

    • Consumer

      • Automotive
      • Consumer Products
      • Retail, Wholesale & Distribution
      • Transportation, Hospitality & Services
    • Energy, Resources & Industrials

      • Industrial Products & Construction
      • Mining & Metals
      • Oil, Gas & Chemicals
      • Power, Utilities & Renewables
    • Financial Services

      • Banking & Capital Markets
      • Insurance
      • Investment Management
      • Real Estate
    • Government & Public Services

      • Civil Government
      • Defense, Security & Justice
      • Health & Social Care
      • Transport
    • Life Sciences & Health Care

      • Health Care
      • Life Sciences
    • MENA Sovereign Wealth Funds

    • Technology, Media & Telecommunications

      • Technology
      • Telecommunications, Media & Entertainment
  • Insights

    Deloitte Insights

    What's New

    • Deloitte Insights Magazine

      Explore the latest issue now

    • Deloitte Insights app

      Go straight to smart with daily updates on your mobile device

    • Weekly economic update

      See what's happening this week and the impact on your business

    • Strategy

      • Business Strategy & Growth
      • Digital Transformation
      • Governance & Board
      • Innovation
      • Marketing & Sales
      • Private Enterprise
    • Economy & Society

      • Economy
      • Environmental, Social, & Governance
      • Health Equity
      • Trust
      • Mobility
    • Organization

      • Operations
      • Finance & Tax
      • Risk & Regulation
      • Supply Chain
      • Smart Manufacturing
    • People

      • Leadership
      • Talent & Work
      • Diversity, Equity, & Inclusion
    • Technology

      • Data & Analytics
      • Emerging Technologies
      • Technology Management
    • Industries

      • Consumer
      • Energy, Resources, & Industrials
      • Financial Services
      • Government & Public Services
      • Life Sciences & Health Care
      • Technology, Media, & Telecommunications
    • Spotlight

      • Deloitte Insights Magazine
      • Press Room Podcasts
      • Weekly Economic Update
      • COVID-19
      • Resilience
  • Careers

    What's New

    • Millennial Survey 2022

      Gen Zs and millennials are striving for balance and advocating for change.

    • Candidate Profile

      After applying for a job in this country, you can access/update your candidate profile at any time.

    • Job Search

    • Students

    • Experienced Hires

    • Executives

    • Life at Deloitte

    • Alumni

    • Diversity and Inclusion

  • XE-EN Location: XE-English  
  • Contact us
  • XE-EN Location: XE-English  
  • Contact us
    • Dashboard
    • Saved items
    • Content feed
    • Profile/Interests
    • Account settings

Welcome back

Still not a member? Join My Deloitte

Decoding the path to purchase

by Tom Davenport, David Rosner
  • Save for later
  • Download
  • Share
    • Share on Facebook
    • Share on Twitter
    • Share on Linkedin
    • Share by email
Deloitte Insights
  • Strategy
    Strategy
    Strategy
    • Business Strategy & Growth
    • Digital Transformation
    • Governance & Board
    • Innovation
    • Marketing & Sales
    • Private Enterprise
  • Economy & Society
    Economy & Society
    Economy & Society
    • Economy
    • Environmental, Social, & Governance
    • Health Equity
    • Trust
    • Mobility
  • Organization
    Organization
    Organization
    • Operations
    • Finance & Tax
    • Risk & Regulation
    • Supply Chain
    • Smart Manufacturing
  • People
    People
    People
    • Leadership
    • Talent & Work
    • Diversity, Equity, & Inclusion
  • Technology
    Technology
    Technology
    • Data & Analytics
    • Emerging Technologies
    • Technology Management
  • Industries
    Industries
    Industries
    • Consumer
    • Energy, Resources, & Industrials
    • Financial Services
    • Government & Public Services
    • Life Sciences & Health Care
    • Tech, Media, & Telecom
  • Spotlight
    Spotlight
    Spotlight
    • Deloitte Insights Magazine
    • Press Room Podcasts
    • Weekly Economic Update
    • COVID-19
    • Resilience
    • XE-EN Location: XE-English  
    • Contact us
      • Dashboard
      • Saved items
      • Content feed
      • Profile/Interests
      • Account settings
    11 November 2016

    Decoding the path to purchase Using autonomous analytics for customer mapping

    11 November 2016
    • Tom Davenport United States
    • David Rosner United States
    • Tom Davenport United States
    • David Rosner United States
    • See more See more See less
      • Tom Davenport United States
      • David Rosner United States
    • Save for later
    • Download
    • Share
      • Share on Facebook
      • Share on Twitter
      • Share on Linkedin
      • Share by email
    • Autonomous analytics
    • Autonomous analytics at Cisco Systems
    • Autonomous analytics and the customer experience

    Given the dizzying amounts of data hurtling across cyberspace each second, companies that wish to glean customer insights through analytics should explore adding autonomous analytics to their sales and marketing arsenal along with plain old intuition and artisanal analytics.

    You could be forgiven for being overwhelmed by customer data. Every minute, for example:

    • There are 3 million Google search queries
    • 140 million emails are sent
    • 5 million YouTube videos are viewed
    • 300,000 Facebook users update their status
    • There are 400,000 tweets on Twitter
    • Amazon.com sells 25,000 items1

    It’s likely that some of those transactions and data involve your customers and your company. But it’s a dizzying prospect to figure out how you can translate all that activity into implications for customer experience.

    In the old days of database marketing and customer segmentation, we practiced what might be called “artisanal analytics.” The bulk of our activities involved generating queries and reports on what our customers had done in the past. On the rare occasions when we employed advanced analytics, we handcrafted our prediction or clustering equations based on human hypotheses and intuitions about what might be going on in the data. When the models didn’t fit quite as well as we liked, we tinkered with them over time (often lots of it) until they got better.

    While some organizations and analysts still employ the artisanal approach, the current flood of data about customer behavior and interests makes artisanal analytics seem almost quaint, and certainly not feasible for understanding today’s customer experience. The data come in far too quickly and in far too large volumes to allow for handcrafted analytics. If we want to respond to the almost infinite number of variations in customer attributes and preferences, we need a different approach.

    Autonomous analytics

    When artisanal analytics can’t do the job, we have to adopt a new set of more autonomous analytical methods. These might take a variety of different forms, but the most common one is called machine learning. Machine learning uses a computer program to automatically employ alternative variables (sometimes called “features” in machine learning) in combination, to create multiple models with an optimized fit to the data. Often the models are trained on data for which we know the outcome on the dependent variable of choice (for example, we know when the customer actually bought something from us). Some versions of machine learning use traditional regression analysis, but they can create many models to fit many different data segments. Other forms use different statistical and mathematical model types. Neural and deep networks, for example, are another type of machine learning that can be used to categorize images and sounds.

    Machine learning has a number of advantages compared to artisanal analytics, and some disadvantages. The primary advantage is that it greatly improves the productivity of quantitative analysis. If there are thousands of different customer segments at your company, machine learning can define them and create models to predict what they will buy.

    Challenges with machine learning include the requirement for a large amount of data to successfully use the method. The outputs of machine learning are also not easily interpreted; they are generally something of a “black box.” This may mean that managers are reluctant to implement models of which they have no intuitive understanding. This is a clear shortcoming of machine learning, but for many organizations the transparency may be worth sacrificing in order to deal with the massive amount of data. It’s a particularly good tradeoff when the cost of a suboptimal decision is relatively low—as with digital advertising.

    Autonomous analytics at Cisco Systems

    About a decade ago, a small analytics group within Cisco’s Strategic Marketing Organization began to help salespeople decide on which accounts to focus their attention while selling particular products. Using artisanal analytics, a small group of analysts developed analytics to predict what customers would be likely to buy. There were only a few of these “propensity models,” which meant that only broad attributes of customers—large company versus small business, for example—could be considered in them.

    About five years ago, as machine learning began to be practical in standard computing environments, Cisco began to generate many more models using the approach. By 2014, the company was generating about 25,000 propensity models a quarter, using data on 160 million businesses around the world. Because of the industrial scale of the modeling, Cisco began to refer to the approach as a “propensity-to-buy factory.”

    By 2016, the factory was generating 60,000 models a quarter. The greater the number of models employed, the higher the granularity and accuracy of the analysis with regard to specific products, geographies, and business characteristics. The marketing analytics team comprised about 20 people, and they were thinly spread across the many models. The computer horsepower Cisco was using had also run low by this point. The sales force had come to depend on the propensity models, and they became frustrated that training and scoring so many models took almost a month of the quarter. But then Cisco adopted some new technology—an in-memory server cluster with open source machine learning software—that sped up the analysis 15-fold. Now it takes a matter of hours, and Cisco is able to use a variety of different machine learning algorithms. Depending on the situation, Cisco sees results of between three and seven times those without the propensity models.

    Autonomous analytics and the customer experience

    Cisco uses autonomous propensity models for sales and marketing, other companies use “programmatic” digital advertising, and still others use the technology for fine-grained and accurate recommendation engines. Some firms are even beginning to use “deep learning” systems to recognize, classify, and act on customer photos from social media postings. Every aspect of the customer experience that involves large volumes of data and the need for a personalized approach is a potential candidate for autonomous analytics and machine learning.

    None of this means, of course, that we still don’t need to develop deep intuitive understandings of what customers want, or that we don’t have to develop creative ideas for marketing and selling to them. But the age of purely intuitive approaches to customers, and even of artisanal analytics to analyze their data, is largely over. Vestiges of them may remain, but the companies that move rapidly to autonomous analytics to understand and structure the customer experience will be more successful in the marketplace.

    Credits

    Written By: Tom Davenport, David Rosner

    Cover image by: David Owens

    Endnotes
      1. Digital Information World, “60 seconds on Facebook, Twitter, Google, Instagram, Tumblr and Pinterest,” http://www.digitalinformationworld.com/2014/07/what-happens-in-just-60-seconds-on-social-media-infographic.html, accessed November 7, 2016. View in article

    Show moreShow less

    Topics in this article

    Customer Service , Analytics , Automation

    Deloitte Analytics

    Read
    Download Subscribe

    Related

    img Trending

    Interactive 3 days ago

    Tom Davenport

    Tom Davenport

    Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Center for Digital Business. He is an independent senior advisor to Deloitte Analytics, Deloitte Consulting LLP. He collaborates with Deloitte thought leaders on all things related to business analytics, from the potential of cognitive technologies to industry-focused explorations and outcomes. Covering topics from emerging technologies to innovative business applications, Tom's Deloitte University Press series reveals leading-edge thinking on analytics and cognitive technology. Connect with Tom on LinkedIn and Twitter. 

    • insights@deloitte.com
    David Rosner

    David Rosner

    Principal | Deloitte Consulting LLP

    David Rosner, Deloitte Consulting LLP, leads the Digital Life Sciences practice in the US. David works with his clients to improve outcomes through the use of digital technologies and approaches driven by analytics and insights. He works with a range of companies within the Life Sciences industry to improve outcomes, engage with patients in valuable ways, and support providers and payers so they can focus on helping patients. David is also the Customer Analytics leader within the Deloitte Analytics.

    • drosner@deloitte.com
    • +1 215 977 7570

    Share article highlights

    See something interesting? Simply select text and choose how to share it:

    Email a customized link that shows your highlighted text.
    Copy a customized link that shows your highlighted text.
    Copy your highlighted text.

    Decoding the path to purchase has been saved

    Decoding the path to purchase has been removed

    An Article Titled Decoding the path to purchase already exists in Saved items

     
    Forgot password

    To stay logged in, change your functional cookie settings.

    OR

    Social login not available on Microsoft Edge browser at this time.

    Connect Accounts

    Connect your social accounts

    This is the first time you have logged in with a social network.

    You have previously logged in with a different account. To link your accounts, please re-authenticate.

    Log in with an existing social network:

    To connect with your existing account, please enter your password:

    OR

    Log in with an existing site account:

    To connect with your existing account, please enter your password:

    Forgot password

    Subscribe

    to receive more business insights, analysis, and perspectives from Deloitte Insights
    ✓ Link copied to clipboard
    • Contact us
    • Search Jobs
    • Submit RFP
    Follow Deloitte Insights:
    Global office directory Office locations
    XE-EN Location: XE-English  
    About Deloitte
    • Newsroom
    • Deloitte events
    • Our blog collections
    • Press releases
    • Press contacts
    • Corporate Responsibility & Sustainability
    • Report an ethics complaint
    Services
    • Audit & Assurance
    • Consulting
    • Financial Advisory
    • Risk Advisory
    • Tax
    • Deloitte Private
    • Legal
    • Sustainability
    Industries
    • Consumer
    • Energy, Resources & Industrials
    • Financial Services
    • Government & Public Services
    • Life Sciences & Health Care
    • MENA Sovereign Wealth Funds
    • Technology, Media & Telecommunications
    Careers
    • Job Search
    • Students
    • Experienced Hires
    • Executives
    • Life at Deloitte
    • Alumni
    • Diversity and Inclusion
    • About Deloitte
    • About Deloitte in the Middle East
    • Privacy
    • Terms of use
    • Cookies
    • Avature Privacy

    © 2022. See Terms of Use for more information.

    Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities. DTTL and each of its member firms are legally separate and independent entities. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. Please see About Deloitte to learn more about our global network of member firms.