Analytics Page 2

25 Jul

Notebook Thoughts – Measuring the Customer Journey

In
By Pedro Laboy
Comment

There are many types of marketing measures that can be applied to a customer’s purchase and experience journey. However, what’s important is that these measures can both determine the effectiveness of specific marketing campaigns and predict the likelihood that a customer will move from one stage of the journey to the next.

Here is a sample of some of methodologies and/or measures that can be used in the different stages of the journey:

NEED

Market Analysis – Determine market needs

Experience Analysis – Determine prospective customer needs and/or opportunities to improve customer experience

RESEARCH

Awareness – Determine level of brand and product awareness

Segmentation – Segment customers to the smallest groupings feasible

Addressability – Level of addressability for each segment

SELECT

Media Mix Modeling – What are the optimal media channels we should use to reach our audiences

Multi Touch Attribution – How are channels performing for given audiences and campaigns

Brand Preference – What is the consumer brand preference within our category

Qualified Leads – Are we getting the desired level of marketing and sales qualified leads

PURCHASE

Propensity to Purchase – What is the likelihood that our audience will purchase our products

Cart Abandonment – Are prospective customers dropping off before completing a purchase

Conversation Rate – What is the conversation rate for our desired actions

Time to Purchase – What is the timeframe from first touch to purchase

Up-Sell / Cross-Sell – Are we able to up-sell and/or cross-sell to our customers

Share of Wallet – What is the share of wallet

Basket Size – How much are our customers spending per purchase

RECEIVE

Propensity to Return – Are our products being returned at unacceptable rates

EXPERIENCE

Sentiment Analysis – How do customers feel about our brands, products, services and delivered experiences

Customer Effort Score – What is the level of effort required by a customer to resolve an issue

Customer Satisfaction – What is the level of customer satisfaction

Respond and Resolution Time – how much time does it take to respond to and resolve an issue

RECOMMEND

Net Promoter Score – What are our customer’s willingness to recommend

Brand Mentions – Is our brand being mentioned positively and at the right level

Revenue Referrals – How much revenue are we generating from referrals

REPURCHASE

Repurchase Rate – Are our customers returning

Frequency – What is the time between purchases within a specific time period

Recency – How many days since the last purchase

Redemption Rate – What are the number of rewards that are being redeemed

Customer Lifetime Value – What is the estimated lifetime value of our targeted segments

OVER TO YOU!

As always, your thoughts and comments are welcomed. What else could we measure at each stage of the journey? What is your approach?

30 May

Notebook Thoughts: Understanding Marketing Attribution

In
By Pedro Laboy
Comment

Algorithmic or Probabilistic Attribution uses statistics and machine learning to determine the probability of conversion across marketing touchpoints. In other words, how much of a conversion should be attributed to each channel. In order to keep things simple, I randomly chose a few variables—out of dozens or more—that could go into our model. Let’s go through the rows one at a time.

Touchpoints – This is a mix that includes online as well as offline touchpoints.

Platforms – These are some of the platforms that can be used to collect the data for each touchpoint.

Cost – Cost is one of our most important variables since it helps us determine ROI. Just because something is effective does not meant it is efficient.

Frequency – How many times was our ad/content served to the prospect.

Action – Did the prospect take any action upon viewing our content (i.e. click on it, etc.).

Duration – What is the duration of an engagement? In this example, our prospect spent 6 seconds on landing page after clicking on a mobile ad. Also, we used technology to determine that the prospect looked at an OHH ad for 2 seconds.

Recency – When was the last engagement before conversation. The closer the recency between touch points the higher the weight to the precedent one. Here we see that the prospect conducted a product search within the hour after he/she engaged a social ad/content. Thus, “Social” would get a larger attribution.

Quality score – This a variable that you don’t see often that is extremely important. What is the quality of the ad/content? Was the ad place next to undesirable content? Was the engagement likely from a bot?

Halo – Was there a “halo effect?” That is, did the prospect take a secondary action of value to the brand. In this example, the prospect did a search for a related product and ended up buying that product in addition to the one advertised.

Data Lake / BI – This is the infrastructure needed to process the data and run the machine learning models.

Attribution – Self-explanatory.

28 Mar

Notebook Thoughts: The Marketing Technology Stack

In
By Pedro Laboy
Comment

 

  1. DATA – The Data Infrastructure employs a data lake to ensure scalability, speed, and flexibility. Data integration covers all techniques to connect all varieties of structured, semi-structured, and unstructured data.
  2. MODELING – The Modeling layer is the environment to facilitate advanced models and analytics. These include: Marketing Mix Models, Machine learning, AI, Deep Learning, Segmentation, LTV, Attribution modeling, and many more. The key to this layer is to have the analytics environment and tools needed to generate these models (e.g. Python and R).
  3. PROFILING – The profiling layer  is the part of the stack in which we attribute data, metrics and measurable behaviors to build segments and individual profiles (in instances where we collect this data). We can also use our data for matching or adding profiles from 3rd party vendors.
  4. PERSONALIZATION – This Personalization layer is the part of the stack which leverages models and data to deliver content and experiences based on behavior and preference. We use the audience information from our DMP and profile creation to store preferences and deliver against those as well.
23 Feb

Notebook Thoughts: A Data Science Approach to Marketing

In
By Pedro Laboy
Comment
  1. Why, how, who and what are we trying to solve for? Are we solving a problem or seizing an opportunity? How does it align to the consumer journey?
  2. Do we have the required data? Are we buying or collecting? Is it structured or unstructured? What about variety, velocity, veracity and volume of data?
  3. What are the appropriate statistical model? Classification, regression, clustering, similarity, etc.? What about marketing models? Segmentation, propensity, etc.?
  4. What is the deliverable and how does it work? Are we delivering through dashboards? Will it need to be real time?
  5. Why should the client move forward with the recommended solution?
  6. What is the investment required?
  7. How are we going to test and optimize our proposed solution?
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