Ecommerce websites that use predictive search notice up to 24% higher sales.
If you don’t use it on your e-shop, you miss the great potential to grow sales, revenue, and profit.
Here’s why (and how) you should use predictive search in your ecommerce website.
What is predictive search?
Predictive search, also called autocomplete, is an ecommerce search feature that provides instant suggestions as users type into the search bar based on their input and browsing history.
For instance, when users google something, they are used to typing and simultaneously reading search suggestions. If a search engine suggests a relevant search term, the person clicks on it instead of finishing typing the query.
You can now find predictive search in all the top tech players’ search engines like Amazon.
Amazon.com autocomplete example
Why is predictive search so effective in boosting sales?
Predictive search can drastically grow business sales by facilitating and speeding up shopping processes.
How?
First, it automatically corrects typos. This way, a user can receive relevant search results despite the spelling errors in the search query.
This feature is especially helpful for harder-to-spell products like bruschettas, panettones, broccolis, zucchinis, and others.
Second, powered with Natural language processing functionality, it understands the search query context and adapts the search suggestion to perfectly align it with the query.
Predictive search is a powerful conversion-boosting strategy that ecommerce platforms can leverage to anticipate and satisfy consumer demands [1].
Why should you consider using predictive search?
Predictive search can immensely shorten the customer journey - from the moment a user hits the search bar to a successful purchase.
As an ecommerce business owner, you might want to invest in an accurate predictive search.
Here’s why:
It improves search accuracy.
Search accuracy directly impacts the likelihood of users finding what they are looking for.
It is very different from traditional keyword-based search engines where the search relies solely on exact keyword matching. If they don’t match, the search engine returns zero results.
Predictive search, however, automatically suggests alternative or related search terms as users type, helping them find relevant products even if they misspell or use different synonyms.
It personalizes the search experience.
Almost every second shopper spends more on websites that personalize their shopping experience.
The predictive search adapts search suggestions based on the context, previous searches, and similar products.
Such predictive search not only offers perfectly query-matching products but also increases the chances of users discovering products they may not have otherwise found.
Several research studies illustrate how predictive models based on web search data can accurately forecast sales in various sectors, highlighting the value of personalized search experiences.
One study accurately predicts car sales using web search data, with a Prediction MAPE of less than 4% [3].
The other study successfully predicts women’s clothing sales volume on Taobao using web search data and a structured time series model, with a mean absolute percentage error of 4.84% [4].
It provides data-driven insights.
Predictive search can generate highly valuable data on user behavior and interests that you can later use to make data-based decisions.
It collects data on users’ search queries, popular terms, click-through rates, and zero-results queries.
You can analyze and use this data to optimize your product offerings and pricing strategies and adjust search offerings to improve the site search performance.
It sets you apart from your competitors.
Ecommerce businesses that provide superior search experience win the majority of the market.
Intuitive, user-relevant predictive search sets your brand as innovative and hyper-relevant. The more user needs you meet, the more happy returning customers you gain.
This ultimately leads to building a reliable business position in the market, attracting new and loyal customers, and increasing revenue.
LupaSearch - your predictive search partner to enhance your search experience
If you are looking for solutions to introduce a predictive search into your ecommerce website, look no further.
LupaSearch, an AI-powered smart search, is a cutting-edge ecommerce search solution that can revolutionize your business by providing predictive search functionality.
With LupaSearch, your customers will experience an enhanced search experience with real-time suggestions as they type, reducing the time it takes to find what they are looking for.
The best news is - you do not need any coding knowledge or a dedicated team of developers. Our team is here to assist you in every step of the integration and continuous improvement journey.
Request a free demo today, and let’s set a path to your business success.
References
- Goel, S., Hofman, J., Lahaie, S., Pennock, D., & Watts, D. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, 107, 17486 - 17490. doi: 10.1073/pnas.1005962107. https://www.pnas.org/doi/full/10.1073/pnas.1005962107.
- Schaer, O., Kourentzes, N., & Fildes, R. (2022). Predictive competitive intelligence with prerelease online search traffic. Production and Operations Management, 31, 3823 - 3839. doi: 10.1111/poms.13790. https://journals.sagepub.com/doi/10.1111/poms.13790.
- Yuan, Q., Geng, P., Ying, L., & Benfu, L. (2011). A prediction study on the car sales based on web search data. 2011 International Conference on E-Business and E-Government (ICEE), 1-5. doi: 10.1109/ICEBEG.2011.5882762. https://ieeexplore.ieee.org/document/5882762.
- Wei, D., Geng, P., Ying, L., & Li, S. (2014). A prediction study on e-commerce sales based on structure time series model and web search data. The 26th Chinese Control and Decision Conference (2014 CCDC), 5346-5351. doi: 10.1109/CCDC.2014.6852219. https://ieeexplore.ieee.org/document/6852219