Challenge

Time and day analysis [TDA] predicts when to send emails to maximise impact. M&S Bank marketing department wanted to know when the best time was to send a newsletter in order to optimise overall engagement performance.


Solution

Our data science team ran a time and day analysis (TDA) on the existing customer data. The team used a logistic regression model that takes real campaign data to predict results which then provides a ‘score’ for each cell (hour and day) and we used this score to create five different classes (quantiles):

  • Very good
  • Good
  • Neutral
  • Bad
  • Very bad

The output involved creating a reusable solution to dynamically send emails at the correct time based on segments using the ‘best time to mail’ information that was established. Allocated send times were incorporated into the campaign workflow and where there was not enough data for a customer they were flagged with the time that is identified as the best performing.

We employed time and day analysis to determine the best time to send.

  • Through TDA we looked at email campaign performance over time in order to determine when customers are more inclined to engage
  • Provided TDA heat maps that were created to show optimal times to send

How we made it happen:

1. Completing an in-depth discovery phase, including on-site discovery workshops to understand the client’s business, objectives, data systems, in-house teams and Adobe Campaign set-up.
2. Scoping and delivery, including:

  • Data accessibility
    We worked closely with the client’s SQL and Adobe Campaign developers to learn about their data engineering and data architecture including schemas, links and input forms. We completed schema edits to make the data accessible and useable, and delivered bespoke training so that similar edits could be completed in-house without our assistance.
  • Campaign production
    We made the categorised data from the data mart available in Adobe Campaign. This allowed the client to use these attributes to build queries in the system that would target customers based on previously determined segments and send out appropriate messaging that they had created.Dave studied the existing email campaign production process – including volume, frequency and range of current and aspirational campaigns. He also assessed the skills and experience of the client’s internal team.Collaborating with the CRM manager and Adobe Campaign developer to understand the full requirements of the campaign, he advised on how to execute campaigns using advance workflow activities.
  • Planning and developing automated journeys
    Dave discussed email content with the client and the drivers that would trigger a set of emails to be sent to a customer – for example, did they sign up on the website or recently stay at a hotel? Together they decided to include five emails in the journey, with the frequency of one per week. They looked at suppression and exclusion rules that would exclude someone from receiving the client’s emails despite meeting the criteria – for instance, if a customer was included in the welcome journey, they would not be part of the BAU experience.
    The next step was to build Adobe Campaign workflows to trigger these journeys, and as part of the monitoring process, implement alerts to guarantee the consistent delivery of emails. Dave also proposed a way to offer social media content, such as recent tweets, within these communications.

The success of the project was supported by:

  • Collaborating face-to-face with the client’s CRM manager, email marketing executive and data team. Dave was the go-to person throughout, bringing continuity, consistency and efficiency to the process
  • Agile working, which cut out any bureaucratic processes and fed into quick response times
  • Knowledge transfer and enablement. This was completed as part of the QA process during the latter stages of our work for the client. Dave discussed every project with the client’s internal team so they could learn to use the platform effectively themselves – including general troubleshooting and making set-up changes. For every proposed solution, he explained the rationale behind the approach and provided the suitable documentation.

Although Dave typically delivered all development work, during the QA phase he showcased the technical set-up to the client and gave some ad hoc training sessions where needed.

Impact

  • The client delivered on its key business objective of maximising use of Adobe Campaign – creating a welcome email and delivering on a previous segmentation project
  • Customer engagement and brand recognition increased through more personalised BAU campaigns and welcome journey triggers
  • Our focus on knowledge transfer allowed the client to build their in-house capabilities – especially to use more advanced Adobe Campaign workflow techniques and personalise emails with data from the database. They were able to see the considerations, follow best practice and replicate Dave’s work to deliver similar projects in the future
  • After receiving our bespoke schema training the client was able to access more information on each customer. They can now make their targeting more effective and improve personalisation in all their future email campaigns

Generated a 100% increase in engagement (both opens and clicks)

TDA has been rolled out across each product

This has now evolved into ‘send time optimisation’ that will send at an individual customer’s preferred time

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