11 Steps to Analyze Data for Campaign Performance

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June 19, 2014

Welcome!

Today’s Topic:

11 Steps to Analyze Data for Campaign Performance

Presenter:

Catherine Magoffin, Sr. Strategist and Team Lead at StrongView

Today’s Agenda

• Using Data to Drive Contextual, Present Tense Marketing Experiences

• Review of the 11 Step Methodology for Data Analysis

• How Data Translates into Contextual Consumer Experiences and Marketing Results

How do we get to the Present Tense?

The Five FoundationalPillars of Success for

Present Tense Marketing

Present Tense Marketing Pillars of Success

1) Acquisition & Revenue

2) Context Awareness

3) Data

4) Efficiency

5) Channel Integration

Beyond Lifecycle = Present Tense Marketing

Present Tense Marketing

Single Channel Multi - Channel Cross -Channel

Evolving the Dialog to the Constantly Connected Consumer

Data Drives Contextual Experiences

Today’s Marketers Need Insight + Action

Insight Action FASTER TIME TO

INSIGHT

UNPRECEDENTED VISIBILITY CROSS-CHANNEL ORCHESTRATION

AUTOMATED INTERACTION

1. Define the question 2. Define the ideal data set3. Define what you can access4. Obtain the data5. Clean the data6. Conduct exploratory data analysis7. Deploy statistical/predictive modeling8. Interpret results9. Challenge results10.Document results and recommendations11.Outline ongoing data analysis plans

The 11 Steps

Objective: Clearly specify the general and specific question you need to answer. This is the MOST IMPORTANT STEP.

Step 1: Define the question

Any question may be a good question . . .

If it supports your business objectives and program optimization goals. Think about questions relating to:

Channel Engagement Device & OS Activity Location Time Demo-Socio-Psycho-Graphic Purchase History Lifecycle Stage Content Preferences Permissions Source Loyalty levels

Step 2: Define the ideal data set

Assuming you have access to anything and everything, define the ideal data set to answer the question.

Step 3: Define what you can access

Realizing you may not have access to every data point desired, what can you get? Think about where it resides, how you can get it and how you can consume it.

Valuable Data Varieties

Step 4: Obtain the data

Go forth and obtain the data in a form you can use.

Step 5: Clean the data

Manipulate the data to be usable in your analysis tools.

Remember to keep a clean copy of the original data you obtained and to describe how you changed it in writing.

Step 6: Explore the data

Begin to review basics of the data:

• Do you have the data elements needed to answer the question?

• Is it accessible by key segments and attributes, such as:

• Program response• Specific timeframe• Brand or product category• Region• Past purchase or Loyalty Level• Source

Step 7: Deploy statistical/predictive modeling

Once you have a basic understanding of the data set, begin to describe the process, relationship or trends the data is revealing. What story is it telling? Where necessary, apply statistical modeling techniques to better assimilate the data.

Step 8: Interpret results

Once you understand the data model or relationship, what does it tell you about the broader question? Can you answer the question now? How does the data answer the question?

Step 9: Challenge results

Before presenting the results to stakeholders, have a data hackathon of sorts -- try to poke holes in the data and your analysis. Do this yourself and have other colleagues provide their input and challenge the results.

Step 10: Document results & recommendations

Finally, present your results, interpretation of the data and recommendations to key stakeholders. Decide on next steps and a plan of action.

Step 11: Document your process

Make sure someone else can come back and consistently replicate the process. Document all steps, save all files and make them available for future reference.

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Examples of Data Driving Success

Analysis and Insight

Analyzing purchase behavior, demographics, location, interests, buyer scoring and other dimensions to assess the impact on purchases.

Data Drives a Contextual Welcome Series

Day 0 Day 4 Day 8

41.83%Open Rate

29.00%

Disengagement Rate

Click-Through

Rate7.68%

Open Rate

Data Delivers Results

Real Estate Relevancy

Gathering Actionable Data

And, More Actionable Data

1. Define the question 2. Define the ideal data set3. Define what you can access4. Obtain the data5. Clean the data6. Conduct exploratory data analysis7. Deploy statistical/predictive modeling8. Interpret results9. Challenge results10.Document results and recommendations11.Outline ongoing data analysis plans

The 11 Steps Recap

Questions?

• Go to www.strongview.com• Whitepapers• Research• Case Studies• Webinars• Expert Advice & Blogs• Twitter: @strongview• Facebook.com/strongview

Catherine MagoffinSr. Strategist and Team Leadcmagoffin@strongview.com650-226-6826