Devising better Marketing Campaign Strategy through Dynamic Time Warping

Ridhima Kumar
May 3 · 4 min read
Image Source: Pixabay

If the words ‘Dynamic Time Warping’ evokes a sense of time travel science fiction, you would not be at fault.

So let us first unpack this science fiction terminology.

Dynamic Time Warping

Dynamic Time Warping (DTW) is a time series analysis technique used for measuring similarity between two temporal sequence. These sequences or time series could be of different length as well.

It calculates the optimal match between two series using a set of rules. Suppose there are two series — A and B. The DTW algorithm minimizes the distance between them by creating a warping path W (now you know where the word ‘warping’ come from in DTW).

How is it different from other distance metrics like Euclidean Distance?

Euclidean distance is useful when both the time series are in sync and will compare the value of series 1 at time t with the value of series 2 at time t. Basically, it does one-to-one match and does not take into account time shifts.

DTW on the other hand applies one-to-many or many-to-one matches in such a way that the overall distance between the two series is minimized. It allows two similar shaped series of different lengths to match.

Image Source:

DTW has been applied in diverse fields such as speech pattern recognition and signal processing. However, the application of DTW in the field of Marketing has been rare.

In this post we will showcase a novel application of DTW in the field of Marketing.

Business Problem:

• The client ran marketing campaign in one market for a period of one year (August 2019 — July 2020) and noticed that the campaign was highly successful in generating incremental leads. This market was termed as ‘Ideal Market’.

• The client planned on implementing the campaign in other new markets (identified as candidate markets) and wanted to identify which of these candidate markets were like the ‘ideal market’.

• The issue was, they did not want to run the campaigns in all the ‘candidate markets’ for a period of 1 year owing to cost concerns.

• The ask was to identify early signs whether a ‘candidate market’ is like the ‘ideal market’ in terms of campaign response instead of having to wait till the year end.


Dynamic Time Warping algorithm was used to understand which candidate markets were similar in behaviour to the ‘Ideal Market’.

  • The client had identified one market as an ideal market where the campaign was successful (Campaign was run from Aug’19 to Jul’20 and leads were recorded for this period).
  • 7 candidate markets were identified by the client to run the similar campaign.
  • For these candidate markets, the leads and campaign data were available for a period Aug’20 — Dec’20 (5 months).
  • The leads from these candidate markets (Aug’20 — Dec’20) were compared with the leads from the ideal market (Aug’19 — Dec’19) using DTW algorithm.
  • The markets with lowest distance w.r.t ideal market were selected to continue the campaign for further period.

Our Recommendation:

  • It was observed that out of the ‘7 Candidate Markets’, there were 4 markets which were most similar to the ‘Ideal Market’ (highlighted in the image below).
  • We suggested the client to continue the campaign in these 4 candidate markets.
Image Source: Author

Result Evaluation & Impact

The client was recommended to continue campaigns in these 4 most similar markets instead of running campaign in all ‘Candidate Markets’.

In the post implementation evaluation conducted in April 2021, it was found that out of the 4 markets suggested by us:

  • Candidate market A, E and F got 5%, 9% and 6% incremental leads, respectively.
  • Candidate market D got 2% incremental leads.

Overall, this solution helped the client in:

  • Correctly identifying similar markets out of a cohort of markets thereby cutting costs.
  • The identified markets netted higher number of leads.

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