10 Things to Win with In-Store Search
Shoppers who use in-store search engine tools to navigate the store are 50% more likely to buy, claims Practical Ecommerce. Stores often encourage shoppers to use search via improving its visibility and usability. Additionally, it is Amazon that is a starting website for consumers who seek product information close to 3x bigger vs. good old Google (based on L2). Consequently, winning in-store search for products is a new name of the game for all brands that want to count.
Search positioning directly impacts sales and is also a marketing medium whose impact is harder to estimate (e.g. research online, purchase offline). Amazon even introduced a Google Adwords-like tool (AMS) for those who want to pay to boost the visibility of their products.
Anyone who uses Google values this engine for an incredible accuracy of the results. Unfortunately, once we get used to Google, we would be disappointed with in-store search engines as most of them are not even close. They do not understand our intentions, even basic grammar or synonyms. Partially, the fault is on the side of the simplicity of the engine itself, but partially due to a poor database of products (parameters, features).
For perspective Google uses over 200 criteria to present to you the results you would value. Below, we present ten common organic (unpaid) criteria that e-commerce stores take into account while they display their results. Please note that stores differ from one another and thus so do the standards they leverage.
1. Search term used in the product description
The most obvious one is to ensure your product contains the actual phrase both in the product title as well as in the body description. Make sure you leverage synonyms in the description if they exist. Double check if the language you use is also used by actual shoppers (and is not your professional jargon).
2. Right product categorization
Often, stores link search queries with specific product categorization and display results simply corresponding with listing product categories. Make sure that your product is well categorized.
3. Rich content
Some stores prefer products with better content over those with poor content as it in general boosts shopper experience. For better or worse, it is usually calculated based on a number of multimedia objects and the length of a body of text. Simply take care of the richness of your content.
4. Number of reviews and star rating
Stores often leverage other shoppers to understand their product preferences. Therefore, a number of reviews may stand for the popularity of the product and motivate the algorithm to show the product highly. Also, the high star-rating should play a similar role.
5. Historical conversion and position on bestsellers list
An even more precise factor for shopper preference is an actual list of best-sellers. Stores often promote products that sell, which is a vicious circle.
The stores aim to do business off your product. Therefore, most of the stores would prefer products that are profitable for them, and profitability may be expressed both as a percent of the price as well as actual dollars the store earns from a unit sold.
Products that are almost sold out or have an excessive stock may also be supported by in-store search results. The store simply wants to get rid of the product, which generates high logistic costs.
8. Near expiry date
If your products are getting closer to their expiry date, the store most likely will find it as a trigger for a higher position in the search.
9. Relationship with the retailer
Selling is a business, and the relationship with business partners is often a game. You may do everything right, but since you would be in the negotiation process with your retailer, your products may be ignored by the algorithms without a clear reason. Although it should not be an important factor, you should always have it in mind when you notice strange behavior in the engine.
10. Artificial intelligence or machine learning
There are large retailers and tiny ones, but most would love to involve machine learning to make their stores smarter and more friendly to shoppers. The only problem is that smaller stores may not have enough database for their AI to learn well enough, so it acts quite randomly. If you have smaller retailer partners that often speak about AI, but your results are weak, consider this as a result.
To sum-up, an in-store search engine is a great booster of your sales, yet it requires a good understanding of the shoppers? behavior, investment in rich content, and effort in building ratings & reviews and finally ensuring a healthy business relationship with the retailer. It seems like a lot of effort, but those brands (even hardly known) which cracked the problem enjoy disproportionate online sales shares.
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