big data and advertising

Cutting through the Hype, Getting to What Matters

PRESENTED BY:

Jed C. Jones, Ph.D.

Co-Founder of MindEcology

Big Data and Advertising:

Mindecology.com

Who Am I?But first, a word about me: Jed C. Jones, Ph.D.: •  Co-founder and chief data scientist at MindEcology, LLC, a data-

driven marketing firm based in Austin, TX •  Heavily engaged in database marketing and digital marketing

for 15+ years •  Worked as a marketer for 20+ years on 3+ continents •  Picked up a few fancy academic degrees along the way •  Fascinated by the place where data, customer needs and

business capabilities intersect

Business Challenge: A national diamond chain store was interested in understanding which online prospect characteristics were the most significant predictors of likelihood to purchase a high-end products within one of their more lucrative product lines Complication #1: The shopper and the ultimate purchaser were not always the same person or household Complication #2: The company had hundreds of data points about shopper behavior on hand and was not sure where to start with the analysis

(to be continued . . . )

Case StudyNational Diamond Chain Store

Agenda

Together, we’ll: •  Define Big Data •  Explore some common Big Data myths •  Explore some Big Data truths •  Share some tips with you on how to use Big Data in

advertising •  Explore a brief Big Data case study •  Share some resources for getting started •  Have some Q&A time

Questions for You: How would YOU define Big Data? What are some of its KEY characteristics? What is the PROMISE of Big Data (real or imagined)?

What is it?What is Big Data?

The 3 Vs….

What is it?Defining Big Data

Variety: Lots of different types of data being potentially used in a combined way to solve the same problem or set of problems

Velocity: Data being created, updated and even made obsolete very fast – often in real-time

Volume: Terabytes or petabytes

Key Big Data technologies:

What is it?Defining Big Data

•  Distributed storage and processing used to crunch lots and lots of data very, very quickly

•  Database management solutions to manage and access the

data

•  Analytics platforms to draw conclusions and make decisions

What is it?

Defining Big Data

Some brands worth knowing:

The Hype

The Hype

BIG DATA. A promising, yet somehow a bit imposing, phrase. Some things you may have heard about it include: •  There will be 40 zeCabytes of data in the world by 2020 (1

ze0abyte is a billion terabytes) (Hortonworks, 2014) •  New technologies are revoluEonizing what we know and can know

about our world (and our customers) •  The Internet of Things (IoT) is coming, which means we will have

access to even more types of customer data to analyze •  You beCer get on board or you will be losing out (right???)

5 Things

Today We Will Explore 5 Things about Big Data that Can Help You Do

Better Advertising

Big Data Myths and Truths

Lesson 01

4 Big Data Myths:

Myth #1: More data is always better Myth #2: It’s a cure-all for your advertising and marketing problems Myth #3: The technologies are smart enough to run themselves Myth #4: Big Data is always better than small (traditional) data


Lesson 01

Truths about Big Data:

Truth #1: Allows you to combine different types of data sets easily Truth #2: Enables you to do find things you didn’t know were there Truth #3: Allows small firms access to processing power that only Fortune 50 companies had access to just a decade ago


Lesson 01

Lesson 02 You Don't Need Tons of Data to Use Big Data Thinking

Don’t have access to true Big Data in your organization? That’s okay because: 1. Even when dealing with much smaller volumes of data (like megabytes instead of terabytes ), you can still leverage the capabilities inherent in Big Data, and 2. You can buy it: leverage third-party providers’ Big Data-driven insights by combining it with your in-house data

Lesson 02 You Don't Need Tons of Data to Use Big Data Thinking

Lesson 03 Focus on the Thinking, Not the Specific Technologies

Technologies will come and go. You will benefit from thinking like an advertising strategist before spending time on the specific technologies. Start by asking: 1.  At which point in the marketing and sales cycle have I identified a problem? 2.  What data can I directly acquire from related to this problem? 3.  What additional data could I add to my existing data? 4.  Which technologies should I apply to my problem? 5.  Which analytical techniques should I use?

Lesson 03

Focus on the Thinking, Not the Specific Technologies

Don’t have your own data science team? Hire one full-time – or outsource it. Here are the advantages of each option:

Lesson 03

Focus on the Thinking, Not the Specific Technologies

Advantages of hiring in-house: •  Anytime access to your data

scientist •  Predictable ongoing costs •  Perfect for recurring type of

analyses

Advantages of outsourcing: •  You are not tied to any single set of tools or

analytical techniques •  Ideal for periodic or custom analyses •  No need to pay for a regular salaried

position or software

Lesson 04Two Kinds of Data:

Directly-Acquired vs. Sourced

Directly-Acquired: Examples: •  Revenue data from purchases •  Geographic data •  Website visits •  Products reviewed or ordered •  Survey data

Lesson 04Two Kinds of Data: Directly-Acquired vs. Sourced

Sourced: Examples: •  Demographic data •  Psychographic data •  Media consumption data

Lesson 05 4 Ways You Can Use Big Data to do Better

Advertising

1.  Monitor Existing Campaigns: Thanks to data gathering software such as Web analytic, POS systems, and coupon codes, acquiring performance data about your campaigns is (usually) pretty easy.

The process: a.  Collect data directly or from media partners b.  View data points as they vary over time or compare against pre-set targets c.  Make better decisions on how to spend your budget

Benefits: Improved efficiencies in marketing expenditures makes it easier for you to gain client and/or C-level executive buy-in for future campaigns.

Lesson 05 4 Ways You Can Use Big Data to do Better Advertising

1.  Monitor Existing Campaigns

Example: Compare cost-per-click data for different Adwords campaigns


2. Test Hypotheses: Test your assumptions about your campaigns to find out what really works. For example, you can run an A|B split test whether your call to action should be “Buy Now” or “Learn More.” The process:

a.  Form a hypothesis by altering a single variable and guessing at the outcome (i.e., if I do X, then Y will happen)

b.  Test that hypothesis – dozens or hundreds of times, if possible c.  Based on result, alter hypothesis and/or update your campaign

Benefits: Helps guide creatives (web designers, graphic designers, and writers) to build effective content. Helps marketing directors fine-tune strategies using facts.


2. Test Hypotheses Example: Send web traffic to both pages and determine which layout actually converts best


3. Build Models: Models can be used to predict the likelihood of future events based upon known data about past events. You can isolate the relevant characteristics of your best customers and then design ad campaigns that push their “buy buttons.” The process:

a.  Isolate the salient facts about your best customers that sets them apart from the rest of your market

b.  Create advertising that speaks to the motivations and “buy buttons” of your best prospects.

Benefits: Provides the basis for generating creative campaigns that are focused on the right type of prospects. Helps creatives, writers, and media buyers target more effectively.


3. Build Models Example: Build customer personas on your best-converting, highest-revenue customers and rank them by historical conversion rates


4. Find New Patterns: Clustering – also called unsupervised learning – involves applying machine learning techniques to large set of variables to find patterns, without starting with a hypothesis. The process:

a.  Collect and arrange variables about customers, products, etc. b.  Run models (e.g., K-means) to cluster them together into natural groups c.  Form hypotheses about why the data clustered that way

Benefits: Helps you spot patterns among customer or product groupings that you never thought were there or could not adequately sketch out. You can then tailor your marketing to each of those segments, separately.


4. Find New Patterns Example: Cluster customers in terms of historical purchases of products or groups of products, then look for patterns as to what makes one group distinct from the next.


Case study

National Diamond Chain Store

Business Challenge: A. national diamond chain store was interested in understanding which online prospect characteristics were the most significant predictors of likelihood to purchase a high-end products within one of their more lucrative product lines Complication #1: The shopper and the ultimate purchaser were not always the same person or household Complication #2: The company had hundreds of data points about shopper behavior on hand and was not sure where to start with the analysis


Solution Architecture: 1.  Data preparation: We pulled the raw data out of the data warehouse. We

engaged in “binning” - or grouping - of the data pertaining to each data point into ranges, resulting in fewer variables required to be processed later.

2.  Neural network machine learning: Arranged the data into input variables and ran them through a machine learning algorithm to find the ideal combination of independent variables that would predict sale vs. no sale.

3.  Tactical marketing implementation: Once we knew which variables were most predictive of a future sale, we implemented new marketing tactics that helped the client leverage those insights.


Result: The process resulted in the isolation of 8 variables that, together, were highly-significant indicators of likelihood to purchase a high-end piece of jewelry. To apply these findings to marketing tactics, we did the following: 1.  On-site Behaviors: we developed special banner ads that could be shown

only to those individuals who were more likely to buy a high-end product; we also simplified the path to the purchase page for all users

2.  Characteristics: we built customer personas that could be hyper-targeted via online and offline messaging


Business Outcomes: •  Increased sales revenue significantly* for this product line,

year-on-year. This was the result of a combination of increased unit sales AND higher average transaction value.

* increase % protected due to client NDA

Case studyNational Diamond Chain Store

Summary5 Take-Aways from Today’s Session

1.  Big Data is Mostly Hype, with Some Very Actionable Stuff, as Well 2.  You Don’t Need to Have Tons of Data to Use Big Data Thinking 3.  Focus on the Thinking, Not the Specific Technologies 4.  Two Kinds of Customer Data: Directly-Acquired and Sourced 5.  Four Ways You Can Use Big Data to do Better Advertising

ResourcesSoftware and Tools for Getting Started:

ResourcesSearch Wikipedia for these keywords:

Hadoop & NoSQL

Books:

Q&A

Questions and

Discussion

Contact

MindEcology.com

[email protected]

Cell 512.796.6519

107 Leland Street, Suite #4, Austin, TX 78704

Thank you!

AppendixMore applications of Big Data thinking to advertising: 1.  Perform text analytics on social media mentions (Tweets, FB posts, etc.)

in order to calculate the % of positive vs. % of negative sentiments 2.  Auto-classify inbound lead form submits into “strong” and “weak” lead

categories, respectively for more efficient salesperson time allocation 3.  Perform a cross-sell analysis to calculate which products available on

your website you should bundle together as special promotions 4.  Build a recommendation engine for your website, in the spirit of: “If you

liked reading ‘Adventures of Huckleberry Finn’, you also might like ‘Treasure Island.’”

5.  Build an optimization model to determine the best marketing mix given your available budget, media availability, and past conversion rates.

Appendix

More applications of Big Data thinking to advertising (continued): 6.  Split test which billboard creative drives more in-store traffic by running

each creative type for one month at the same location. 7.  Build a site selection model for a restaurant that caters to a nearby

business district. To understand passers-by, leverage 100s of gigabytes of mobile phone geo-location data to extrapolate demographic and psychographic info based upon where they go home to at night.

8.  Build a color-coded heat map of where your best prospects live in the highest concentrations within your trade area.

9.  Build a model that predicts which shoppers are college-age males based upon purchasing habits.

big data and advertising

Data & Analytics

big data truths

big data variety

zecabytes of data

common big data myths

big data distributed

promise of big data

types of customer data

chief data scientist