optimization as a golden layer - chris diener, svp analytics, absolutdata

Optimizationas a golden “layer”

Presenter: Chris Diener, SVP – Analytics, AbsolutData

MRA Insights and Strategies ConferenceJune 10-12, 2013

Venue:

Chris

We can include the Absolute Data logo in the upper right corner of all slides. And I like this slide. Let's repeat a version of this picture or imagery in the final slide too.

Vision

Mission

To become the

Most Impactful, Most Respected,

Most Powerful

Analytics Firm that has ever existed

To help

forward looking organizations excel

Through optimal use of data

Chris

This is our one slide I want to use to introduce AbsolutData -- we probably need our logo and name here on the slide.

Recommendation Systems

Constrained Optimization

Linear Programming

Machine Learning

Genetic Algorithms

These are the tools of the 21st Century

Ever Heard of This?

Chris

Clean this up according to the rules I have outlined. Put text on the right and use that kinetic machine picture I talked about on a prior slide here but cut out a piece of it and make it the left hand side of the slide.

Are You

Optimizing?

MR Industry Conditions – Tough!

Client perspective

Order takers instead of consultants

Do the work instead of liaising and thought partnering

Questions of relative contribution compared to “Analytics” folks using non-survey data

Vendor perspective

Vendor perspectiveCommoditized: lack of true differentiation of services

Self-help explosion

Budget squeeze between economy and “Analytics/Social” spend

Chris

Lets find another image that represents different perspectives -- I don't think the big/little imagery is right for this audience. There will be both vendors and clients in the audience. Good first effort lets just work at another image. Or we can find an image that represents how tough it is so some image that shouls gritty hardship? Also for the final run we need to get all fonts to be at least 23 pt.

How do you differentiate?

How do you win?

Problem: An Insight is not an Insight, is not an Insight

RESEARCH

MUST

MUST NOT

Impact Decision Making

Remove specific pain or solve a specific problem

Merely Describe

Just address issues or answer questions

Chris

From all slides remove the initial bullet points on the main point and we can probably make do with just the indented other items without the sub bullets unless there is a wrap around -- maybe instead we just do separate text boxes. That would probably look better and less outline-like, so lets do that where it makes sense and avoid bullets.

What do they need

to do?

Virtually all research falls

short

Recommend! (make a stand, be an expert, be specific)

Dig! (identify specific decisions

and actions)

How do you do this?

Support! (with a story)

Chris

Again as a general comment across slides -- lose the bullets and either do no bullet or space the statements enough or just do separate text boxes for each line. Also this image is a bit too busy with the text. Maybe you can come up with an image of a big digging thing -- a huge mining operation, or huge machine that drills or something.

Ensures Recommendation

Empowers Recommendation

Optimization

Make you a more valuable resource

Promotes Specificity

Chris

Unfortunately this looks too clip art like. Let's find a realistic image or HD image of some kind. Maybe a close up of the turbo fans of a jet ingine? Something like that which shows complexity, power and finesse or detail.

Bringing a System to its Peak Performance

Need constraints Need a goal

Need a system – or a Model

Searching and recommending

Need levers to pull to get to that goal

Chris

Lets have a composite image going here where we have five areas of teh slide other than the title and in each area we have not only the statement but then an photorealistic image for each of the five elements. Let's have it build.

DATA

Identification of critical outcomes and management levers

Building a model to find the predictive

relationships between levers and

outcomes

RECOMMENDATIONS

SIMULATOR ENGINE

Potential constraints

Conjoint Product and Product

Line Management

Segmentation For communications

or product development

Targeting Database or

Demographic Classification

PositioningBrand Image Management

Examples of Optimization

Chris

Choose another background for this one if you use this for the prior slide. For the prior slide or this one you may want to find a picture of one of those marble kind of kinetic engines that dazzle kids where a ball rolls and hits something that then leads to something being triggered that that then leads to another whirlygig doing something etc. Those are fascinating systems. I think you can search for it under kinetic system or something like that.

How Optimization Ensures Differentiating Quality of

Work

Segment prioritization

Brand positioning

Conjoint

Chris

Include graphic Simulator page picture. Here as the main background element. I like this one though so lets find another place for it. I have inserted what I want -- now just organize it to look good!

Descriptive

Segmentation

Fashion Focused – 16%

Young Passionate about

fashion Keep up with the latest

trends Own relatively higher

number of jeans Willing to pay more

money for her jeans

Bargain Hunters – 28%

Interested and involved in this category

Love to shop but on a budget

Actively looks around for deals and sales

She own an average number of jeans in spite of spending less than average on jeans

Classic – 26%

Middle aged working women

Though she is not very style focused, she doesn’t want to look out -dated

Wants classic & latest trends to suit her age

More likely to buy specialty store brands such as Gap & Old Navy

Utilitarian Buyers – 20%

Lower income, white More inclined

towards functionality than style

Buy jeans only when necessary

Budget conscious Shops at discount

stores Buys Lee Jeans

Urban Dresser – 6%

African American and Hispanic

Want to look hip and trendy

Willing to pay more Large share of

purchases in urban brands

However more likely to buy jeans on sale and in off-price stores

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Chris

For this slide let's use one of our example outputs for segmentation as the first descriptive example slide and then let's use the chart on the left below as well as build out a table that has the elements described in the word document outline for the second slide in the series.

Prescriptive

Segmentation

S6

S1

S3

S5

S2

S4

S12

S8

S11

S7

S10

S9Ability to Win

Attractiveness

Higher

Lower

Lower Higher

Segment Prioritization Matrix: Growing Brand Share Go after Segment S12 first and then consider segment S4 as a secondary target

Chris

For this slide let's use one of our example outputs for segmentation as the first descriptive example slide and then let's use the chart on the left below as well as build out a table that has the elements described in the word document outline for the second slide in the series.

Conjoint

PrescriptiveDescriptive

2%

4%

8%

13%

18%

24%

Margin Impact

1%

2%

3%

4%

6%

9%

Sensitivity= -1.61

Price product at $19K and offer a 5-year Warranty

Chris

This is the sort of structure I'm looking for and then I want pictures showing actual examples of each as I outlined in the word document.

PrescriptiveDescriptive

Quick dry

Fresh all day

Dry all day

Skin Comfortable

Refreshing

Suits Sensitive Skin

Confidence Giving

No Stickiness

Effective against Odour

Low Cost

Low Cost

No Stickiness

Confidence Giving

Suits Sensitive Skin

Refreshing

Comfortable on skin

Dry all day

Fresh all day

Quick dry

Brand positioning

Spend $50M to:• Increase Comfortable by 2 pts• Increase Confidence Giving by 5 pts

Chris

We need a brand image bar char with more bars -- lets go for hoizontal bars and 10 statements to the left of the bars. For the proscriptive area build the map out as described -- just use dummy data. In fact break these out to separate slides and do that for the others as well so I can show descriptive and then flip to proscriptive. With the proscriptive on this slide in the map circle the top right quadrant and say "Move these attributes"

Finding the best product to make

Finding the first three product changes to make

Knowing who to target first and with what message

Knowing how much to invest in raising awareness of which specific brand qualities

Key is identifying a Goal that is Actionable

Chris

Follow the format on this slide for all others -- text on right and graphics on the left and remember to keep applying my rules about no bullets

Frame your research in terms of specific decisions

Identify critical decision criteria that can be the basis of a maximization goal

Define the relationships between levers and the goal

Using the model, search for lever combinations that get the best goal outcome

How to do it

Recommend the best lever combination(s)

Chris

The picture looks good. Lets move it to the left side (as said before) and lets lose the bullets but instead make it a process kind of thing maybe building stages of a Key or something instead of bullets -- make it graphical and intriguing.

Optimizing Using Social Media

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Using Social Media crawlers such as Buzzmetris, Radian 6, Alterian to collate unstructured user generated data from Social Networks, Blogs, Forums etc

Use Text Mining software's or custom verbatim coding methods to create quantitative data for reporting and modeling

Mining Social Media Data

Text Mining

Brand Tracker Report

Level 1 ModelSentiment Drivers

Level 2 ModelBrand Metric Drivers

Report Social Media Brand Metrics such as Brand Sentiment Score, Awareness / Share of Voice (SoV) along with visuals/frequency for Themes/Topics, Imagery, Brand Attributes, Product Attributes, Source etc

Multivariate Regression model to identify key drivers of the Brand Sentiment in the Social Media comment at Author (Respondent) Level as DV and Theme/Topic, Imagery, Persona, Brand/Product Attributes, Source etc as Independent Variables

Multivariate Regression model to identify key drivers of the Brand Performance Metrics (Brand Equity Score from Traditional Research, Sales/Volumes) aggregated at Week/Month Level using week level SM Metrics - Brand Sentiment/Satisfaction, Brand/Competitor SoV, Imagery, Personas etc

1

2

Details on the Driver Models

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Reporting and Sentiment Driver Analysis Brand Key Driver Analysis

with key drivers of Consumer Sentiment drivers of Key Brand Metrics

Weekly dashboard withSM Frequency at category level• Brand Share of Voice/ Awareness• Competitor Share of Voice• Sources of conversation• Brand Sentiment Score (BSS)• Competitor Sentiment Score• Conversation theme cloud• Imagery (Existing and Emergent)• Product/Brand Attributes• Key Influencers

Drivers Analysis using Multivariate Regression

BSS = f( Imagery, Product/Brand Attributes, Benefits , Personas, other themes)

Aggregated Metrics from various sources (Weekly/Monthly buckets)• Weekly/Monthly Sales or volumes• Brand Equity Score (BES), Purchase Funnel Metrics from Traditional Research• Macro Economic Factors (US Gov, BLS)• Traditional Media Spend• % Share of Voice (SoV) - Brand, Competitor• Brand and Competitor BSS• Number or % mentions for imagery, Attributes, key themes• SoV by Personas etc

KDA using Multivariate Regression

Sales/Volumes or BES= f(Macro Factors, SoV variables, Mentions or Share of imagery/Attributes/ themes)

1 2

Optimizing product, people and message in the same system

Advanced Optimization Applications

Inclusion of social media or other big data with survey data for better application and insight

Segmentation

Targeting optimization

Brand purchase driver modeling

Marketing effectiveness and attribution modeling

Path to purchase modeling

CRM lifetime value calculations

Promotion optimization in direct marketing

So/Lo/Mo models for better targeting

Chris

The background graphic is cool. The way the text is situated looks confusing. So, let's lose the bullets first. And maybe we can make this a build such that it starts with just the picture and the title and then it brings in the other points. These maybe can be black boxes with yellow text or white.

If you need help with Analytics or Research, please write to us:

[email protected]@[email protected]

For Media related queries [email protected]

For all other queries [email protected]

25

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What does your research look like?

Thank You

Where is your GOLDEN LAYER?

Chris

Use original image we chose for first slide

optimization as a golden layer - chris diener, svp analytics, absolutdata

Technology

absolutdata venue

recommendation optimization

conjoint product

need levers

day comfortable

specific problem

specific decisions

specific brand qualities