#11/25: Principles of Marketing Engineering

Marketing Engineering: A systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process.

Market response models

  • Inputs: actions that the marketer can control + environment
  • Response model
  • Objectives: monitor/measure, etc.

Value = Benefits – Price

  • Benefits
    • function (does the job)
    • psychological (status)
    • economic (saves money)
  • Price
    • Monetary
    • Perceived risk
    • Inconvenience

Customer Need

  • subject and importance of need
  • temporal aspects of need (urgency, frequency and duration)

Measuring Customer Value

  • Objective Customer Value
    • Internal engineering assessment (internal estimate)
    • Indirect survey questions (pay for better quality, etc.)
    • Field value-in-use assessment (economic benefit): Not only marginal costs but initial investment, etc.
  • Perceptual Customer Value
    • Focus groups
    • Direct survey
    • Importance ratings
    • Conjoint analysis
    • Benchmarking
  • Behavioral Customer Value
    • Choice Models
    • Statistical models / Data mining

Segmentation -> Targeting -> Positioning

  • Creating
    • how does the segmentation fit into the strategy
    • which variables can be used
    • exclusive segments?
    • how many segments?
  • Traditional segmentation
    • reduce data (PCA)
    • develop measure of association
    • identify and remove outliers
    • form segments (cluster analysis): are they clear and robust?
    • profile segments & interpret

Positioning

  • Attribute-based perceptual maps
    • identify other products & attributes
    • get data from questionnaires
    • reduce
    • plot
  • Preference Maps
    • weights for attributes
  • Joint-Space Maps

Translating Preference to Choice

  • first choice rule: infrequent, expensive
  • Share of preference rule: often, cheap, etc.

For [target segment], the [offering] is [positioning claim], because [single most important support].

Forecasting

  • Judgmental Methods
    • Sales force composite estimates: let the sales force forecast
    • Jury of executive opinion: different stakeholders
    • Delphi method: anonymous and iterative
    • Chain ratio method: split up in its factors

Conjoint study

  • select attributes of product
  • develop bundles
  • do survey
  • segment customers based o part-worth function
  • design market simulation
  • select choice rule
  • adj market shares

Top 10 Lessons

  • Marking Engineering is Marketing
  • ME is a means to an end
  • frames the opportunity costs for alt. actions
  • requires judgment
  • whole greater than the sum of its parts
  • data & info do not automatically result in value
  • rapid prototyping
  • every model has its downside
  • ME requires lifelong learning
  • be a coach rather than a teacher

Insights for better Implementation

  • Be opportunistic
  • Start Simple; Keep it Simple
  • Work backward
  • score inexpensive victories
  • develop a program, not just a project

I really liked Principles of Marketing Engineering. The book gives a great overview over the topic and different approaches. It’s written like a textbook, so it can be a bit lengthy in parts but otherwise, a nice book.

Reading Adobe’s Digital Marketing Blog (Part 2)

Avoid “anticipointment”: bridging the gap from ad to site

  • Ads and web site work together – don’t just invest a ton in one medium
  • Marketeers fall easy into the ad trap because it’s easier than creating an usable, engaging web site
  • People expect that the click from the ad will be of even more value than the ad
  • Online Marketing Value Chain: Basically Customer Lifetime Cycle
    1. Click ad, engage deeper in the landing page
    2. Make their way through conversion opportunity
    3. Become loyal customer
  • Most of these steps will be on the web site!

Creating a Successful Lead Nurturing Strategy, Part III: When Should I Call?

  1. Call within 5 minutes of the initial contact
  2. Call early at morning or late in the afternoon
  3. Call on Wednesday or Thursday – I personally tried this against Monday and Friday and it was highly effective
  4. Call them up to four times and send one email in the first 24h
  5. Test these tactics

Creating a Successful Lead Nurturing Strategy, Part IV: Your Long-Term Strategy

  • The main is not to sell but to maintain a relevant conversation
  • Offer relevant and personalized content – recent study showed that most content simply sucks, so watch out
    • Email – automated, personalized and relevant; reports, tips, guides, best practices
    • Phone – Follow up; provide deeper information, answer questions
    • Direct mail – reinforce what you’ve talked about; again personalized and relevant
  • This process should be repeated maybe once a month

Optimization Is Greater Than the Sum of Its Parts

  • Testing & Targeting are greater than just once
  • however often they are siloed
  • Start with testing and segment the results
  • This helps you to find better content for targeting

Building a Business Case for Optimization

  • Biggest problems are processes and politics
  • They hadn’t ownership over the site
    • Testing generates positive ROI!
    • Optimizing landing pages increases off-site ROAS (Return On Ad-Spending)
    • Test to fail faster – some of your assumptions are probably wrong
    • Dig into analytics, segment and provide insights

The Collaboration of Testing Ideas

  • Include other people and departments in your testing
  • Often people in development, IT, creative, etc. have ideas – just ask them
  • Test Ideas:
    • Test different landing pages: home page, product page, internal search, etc.
    • Reinforce ad text/graphics on the landing page/multipage setup
    • Test ads
    • Test incentives for submitting to your email database
    • Test emails
    • Build a story with the ad and following pages
    • Test different viral/referral elements: coupons, vouchers, …
    • Test different forms
    • Test % Off vs. $ off
    • Test your CTA copy, size, color, style
    • Test scarcity on offers
    • Test different copy approaches: informative, funny, benefits oriented, etc. and analyze segment behavior
    • Test signs of trust: security message, shipping info, return policy, etc.
    • Test geographical targeting
    • Test simple content vs. rich media
    • Test content vs. no content
    • Test free shipping vs. % off vs. $ off vs. guarantee vs. …
    • Test promotion tresholds: 10% on $50 vs. 15% on $100
    • Test different internal search results – hand picked, automated, editor picks, big brands, cheapest first, best selling first, highest rating first, etc. and segment(!)
  • Strategies
    • Understand your goal – what are you’re trying to improve?
    • Start with the bottom in your funnel – it’s easier to get more impact
    • Try to understand why alternatives work better
    • Try to improve one theme at a time, e.g. decrease registration drop off, copy style, etc.
    • Focus on big things: product shown, pricing, primary copy, images, offer, CTAs

Five Times to Test: 1 — When you need to optimize beyond the click

  • Data without analysis and communication is not very useful
  • Even then without taking action, it’s practically useless
  • Often lots of money is invested in driving traffic but less in converting the traffic
  • Example: large business $100MM PPC budget, less than $200k for optimizing landing page/website
  • Mark Typer, Wunderman: 15% Optimization, 85% Ad spending

Five Times to Test: 2 — To resolve internal disputes

  • Do you have a dispute? Just test the idea
  • Similar things can work different on different websites, e.g. CTA wording

Reading Kaushik (Part 3): Strategic Analysis

Path Analysis: A Good Use of Time?

  • Customers like the path the want to, not the one you force
  • The tool don’t show which page in the path was most influential
  • Most tools don’t track the path correctly
  • Exception: Landing Page experience
  • Possible solution: Group relevant pages
  • Show most influential pages/li>
  • Make segmentation easier

Stop Obsessing About Conversion Rate

  • Overall conversion rate doesn’t allow for actionable insights
    • It only covers a small minority of all visitors
    • People are going to research on your site
    • People who want to learn about your company
    • People who need help with a product of yours
    • People that do something completely different
  • That is, you neglect a big part of your visitors
  • You’ll focus on short term gains
  • Better metric: Task Completion Rate
    • Helps you to cover all customers
    • Successes outside from conversions
    • Ultimately understand your customers better

Getting Started With Web Analytics: Step One – Glean Macro Insights.

  • Understand the macro level first
  • 1. How many visitors are coming to your site?
  • 2. Where re they coming from?
  • 3. What is the purpose of your website? What are your top three web strategies you currently working on?
  • 4. What are you visitors actually doing?

Consultants, Analysts: Present Impactful Analysis, Insightful Reports

  1. No data overload: Give value instead of data, provide recommendations
  2. Tie your data to business outcomes
  3. Use other data than just Clickstream
  4. Don’t make it boring
  5. Connect insights with actual data
  6. Meet the “exceptions of scale”: If you are a big agency or written a book on WA, then people expect more from you
  7. Do something unique

Paid Search Analytics: Measuring Value of “Upper Funnel” Keywords

  • Upper Funnel / Longtail Keywords can neglected because of the single session mindset
    1. Understand each stage of the customer purchase life cycle
    2. Map your keywords to each of those cycles
    3. Measure success for each cycle differently
    • Category Keyword: Bounce Rate
    • Category / Brand: Time on Site
    • Brand: Visitor Loyalty
    • Conversion/Product: Conversion Rate / Leads

Aggregation of Marginal Gains: Recession Busting Analytics!

  • Often web analysts don’t focus on the immediately achievable improvements
  • Simple things:
    1. Figure out where you are making money
    2. Check errors in your email campaigns
    3. Use funnels
    4. Fix your top landing pages
    5. Compare organic and paid keywords: Where are gaps between these two and why?
    6. Ask your customer
    7. Fix dumb stuff: e.g. check 25-point Website Usability Checklist

Analyze This: 5 Rules For Awesome Impromptu Web Analysis

  • Question: What would you change on this website?
  • Useful things to remember:
    1. Don’t start with your opinion: you are a proxy for customers / visitors => Better: State hypothesis
    2. Always offer alternatives
    3. Offer data, even when you don’t have access to the site’s data.
    4. State your assumptions about the site’s objectives
    5. Focus on obvious and non-obvious things: micro and macro conversions, competitor’s site, customer satisfaction, ideas for testing, demographic trends, etc.

Web Analytics Segmentation: Do Or Die, There Is No Try!

  • Pick at least some segments in: Acquisition, Behavior and Outcomes
  • #1: Acquisition: Where does the company spent its most money on?
  • How to segment:
    • Context: How many visits?
    • (Optional): How many were new?
    • Bounce Rate
    • What was the cost of acquisition?
    • What value could we extract at a per visit level?
    • How many were able to accomplish their goal?
    • Was what the total value added to our organization?
  • #2: Behavior
    • What are the visitors doing?
    • What do people want to do on your site?
    • How deep are they browsing on your site?
    • How long did they take time till conversion?
    • How often did they visited your site?
    • => Traffic source, conversion, average order value, actions, etc?
  • #3: Outcomes
    • Loot at macro and micro conversions!
    • i.e. video clicks, adding products to wish list, applying for a trial, downloading white papers, etc. etc. etc.

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two

  1. Go deeper — Don’t stop at the obvious border: compare off and online data, create CLV for ecommerce,
  2. Join the People against lonely metrics club
  3. Measure the complete site success
  4. Don’t just report one month: at least three months, understand your business’s cycles, create annotations
  5. Make insights in your data obvious: Better visualizations
  6. Segment your data (previous summaries)
  7. Don’t just look at the top 10 rows
  8. Step away from one-session thinking (later summary)
  9. Achieve multichannel analysis (previous summary)

Win Big With Web Analytics: Eliminate Data & Eschew Fake Proxies

  • Most reports are overloaded with data
  • Though, the readers want context and insights
    1. Eliminate all useless metrics and data in your reports
    2. Understand the desired outcomes

Rebel! Refuse Report Requests. Only Answer Business Questions, FTW.

  • Context is important – no business is the same
  • Answer business questions – what’s the driving request for the data?
  • Attributes of a business question:
    • They are usually open-ended and on a higher level
    • They need likely more information than just visits, bounce rate, etc.
    • They are seldom answered with tables

The Difference Between Web Reporting And Web Analysis

  • Data puking isn’t web analysis
  • Signs that you are doing web analysis:
    1. Actions instead of data
    2. Work with the business, measure economic value
    3. Use the web analytics measurement model (previous summary)
    4. You are doing a bit advanced statistics
    5. If you work with targets
    6. You provide context
    7. You segment your data effectively
    8. If you can provide an impact for a recommendation
    9. If you use less than four metrics in a table
    10. If you use multiple data sources

Video: Successful Web Analytics Approaches by Avinash Kaushik

Again a great video, this time about Web Analytics by Avinash Kaushik. I just love his no-BS style.

  • Ask the metric: So what? Three times, if it don’t give an action it’s useless
  • Data should drive action
  • Give people the information they need – don’t send them everything => no death by data
  • Home pages of websites, are no longer the home page you want
    • Where do people come from?
    • What are they looking for?
  • Context matters: previous months, years, etc.
  • Relative numbers more important than absolute numbers
  • Compare different metrics, e.g. conversion rate and page views
  • non e-commerce sites:
    • averages hide truth effectively
    • How often do they visit?
    • How recent did they visit?
    • Depths of visit
    • => Understand the value: Loyalty
  • Segment people
  • Survey people: What do they think about the content?
  • Bounce rate: Came and left
    • Segment by source, entry-page, landing pages, etc.

Rules for Revolutionaries

  • 10/90 Rule: $10 Tools, $90 People: Understand Data & Business, Able to analyze => to extract value
  • Reporting is not analysis: Reporting -> provide data; Analysis -> prove insights
  • Data Quality can be low, but is still better than other data
  • Faith-based initiative: e.g. magazine ad without tracking
  • Make decisions, don’t argue about the quality of the data
  • Over time understand why quality is different -> confidence will get better

    Conclusion

  • Decision making is a journey, not a destination
  • => Put some level of process in place, mostly for tasks, e.g. what happens to implement a test, etc.
  • if HiPPo (highest paid person’s opinion) makes the most decisions
  • => make experiments
  • Learn to be wrong, quickly
  • => You probably don’t know what your customers want
  • => Experimentation