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We need to understand our customer journey in order to identify opportunities for optimization.

The Fresh Tracks customer journey is not linear and comes with certain complexities when mixed with the online world. Customers are met with a lot of information during their decision-making processes from CPC campaigns, emails, trip pages, and FTC agents. It is important to understand how those working on web, marketing, and sales teams gain a common understanding of their customers and their end to end journey.

This Click to Cash framework provides an in-depth analysis of the Fresh Tracks customer journey that begins with engagement with inbound marketing campaigns and ends with the sales conversion on a booked trip, in order to identify improvements on:

  • % paid campaign to trip page view conversion rate
  • % page view to inquiry conversion rate
  • % inquiry to sale conversion rate

A dashboard was created based on the above objectives in order to establish what aspects of the current data could be used to drive click to cash reporting. The following are samples of specific areas within the analytical funnel:

  1. Paid campaign to trip page view analysis
  2. Trip page view to lead inquiry analysis
  3. Inquiry to Sale Analysis

Data Discrepancies
Issues ranging from inaccurate counts or missing information were identified as roadblocks in the creation of the click to cash funnel. The following are examples of data discrepancies that need to be solved:

  1. Records Missing from Marketo
  2. Inconsistent Booking Dates Between Marketo and Legacy System
  3. Duplicated Legacy Booking Record with Single Record in Marketo
  4. Bookings with no URL String in Marketo

It is important to set a good foundational structure to ensure that the correct information is captured for the click to cash funnel. The data discrepancies need to be addressed, as well as the reporting of specific touchpoints throughout the customer journey. The following phases ensure future analytic work has a reliable and insightful outcome:

Phase 1: Foundational Structure
Understanding what the current data structure is today and enhancing it so that it can elicit consistent insight for business intelligence. This is done through various discoveries:

  1. Data Discrepancy Cleanup Discovery: In the initial stages of establishing the click to cash reports we found some inconsistencies with the data being analyzed.
  2. Current Systematic Discovery: Identifying what modifications are required to use legacy data that is structured/created foundationally different in the SFDC system and how to align these data discrepancies moving forward. This discovery will look at issues such as currency variation, revenue splits, and date variations, etc.
  3. Chat and Call Discovery: The handling of Chat and Call leads needs to be established. First, we need to look into how they are currently being handled, then we need to look into the process of integrating them into the system. The overall alignment of these processes and data points needs to be established and managed.
  4. Normalization Strategy Discovery: There are a number of data points that change throughout the progression of the customer lifecycle’s data points and timeframe. These changes are unavoidable, but a way to normalize this data to ensure usability in the long run is required. This data reference table will be critical for analytics moving forward.

Phase 2: Proof of Concept (POC) Buildout
Creation of 2 different POCs (1) Top of Funnel (web journey) 2) Mid/Bottom of Funnel (lead to sale journey) will be drafted once the data discrepancies have been addressed/mixed.

These will be QA tested individually and then merged to build the third POC.

Once merged, it will be QA’ed in its entirety. Outcomes of this final POC will work to summarize and confirm the data and architecture requirements, analytical principles, management guidelines, and sources for the marketing data within a future data warehouse.

Phase 3: The Third POC

  1. Connecting Data from PPC Ads, Web Analytics, and Marketo
    These three data sources are currently acting in siloes, so the first step would be to ensure that we have the ability to analyze the data cross-functionally. This will allow us to more accurately measure the click-to-sale conversion probability and path of a lead. In order to do this, it is important to ensure that data is connecting from Web Analytics to Marketo/Salesforce. This is done through the creation of a data pipeline. The data pipeline is the technology or process that extracts data from all of your platforms and makes it ready for analysis.
  2. Integration of BI Tools
    The data then needs to be integrated with Business Intelligence (BI) tools. This will lead to the creation of our click to sale report (where all the real value from the data comes from). This POC will prove out the framework strategy in its ability to showcase the insights required by the business for click-to-cash reporting.

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