Use Case

Internal search optimization

Get granular insights on how users navigate using search

Approximately 30% of ecommerce site visitors use internal search, and the purchase intent shown by internal searchers can lead to up 5x higher conversions (Econsultancy).

This also extends to other business models, such as marketplaces or aggregators which actively monetize search outcomes and positions. Two great examples are job boards and automotive listings.

Regardless of your industry, however, optimizing internal search experiences can have a huge impact on customer retention and profit margins.

Why is internal search optimization tough?

Understanding the state of your internal search requires a clear picture of a users journeys through your site, including the role search played in that journey.

For companies like Autotrader, which have anonymous users and long purchase cycles with many different search sessions, optimization is a huge challenge.

  • How can you understand users you haven’t identified?
  • How can you track accurately with ITP restrictions?
  • How can you link search to wider composite metrics like Customer Lifetime Value (CLTV) to understand how different segments use your search?

If the user journey is lost, then so is your chance to improve their experience. Simply put: poor quality or irrelevant search results lead to users churning off your product.

See our full case study on Autotrader’s journey to advanced analytics and AI.

(Image of internal search visualization in Data Studio, courtesy of Search Engine Watch)

What can you achieve with better internal search functionality?

Behavioral data allows you to see how your users interact with your search second by second, with each interaction recorded as an ‘event’, i.e., a row of information with contexts (entities and properties) added.

An example might be:

Event = User search.

Entity = User (female, 34, USA, etc.)

Event Properties = search number, session number, user intent, item type…” and so on (Snowplow has 140 out of the box, and unlimited custom entities and properties).

This behavioral data needs to be accurate and granular:

  • Accuracy allows you to stitch users across devices, channels, and platforms without significant errors which could skew your results.
  • Granularity means you have enough metrics to ask ever-more interesting questions, such as “are users starting with one intent and buying a different product?” (here’s a technical read on user intent).

This better search experience has several outcomes:

  • Delivery of more relevant search results, which leads to great user satisfaction
  • Raising ‘clickthrough’ on top-ranked results, which drives traffic to high-value parts of your site/app
  • Integrating conversion results into the search ranking
  • Removing the need for error-prone and expensive human intervention, cutting development costs and permitting greater automation

Customization is key to optimizing internal search

Companies with better data which is well understood outperform the rest. Your search engine is not a commodity – it’s core IP for your business. Using the same standardized search analytics as your competition makes innovation extremely challenging.

Customization means shaping your data to your own context; this inherently distinguishes your offering from other products.

Why choose Snowplow for internal search optimization?

Snowplow is a warehouse- and lake-first behavioral data collection tool, which allows you to create powerful data models with tools like dbt.

These models can be used to take internal search analysis to the next level, using metrics from across the business to optimize the customer search experience and promote your best products more effectively.

  • Snowplow data is ultra-granular and accurate, meaning you can slice and dice it to answer more complex questions
  • Our unique Data Creation approach allows you to fully customize your data for your context
  • You can avoid ITP restrictions to get up to 400 days tracking of users, meaning you get a complete picture of Safari customer journeys (not the case with the vast majority of analytics tools, which limit this to days)

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