So what is Local Search you ask? Local Search makes use of specialised internet search engines that allow users to “submit geographically constrained searches” against a structured database of local business listings. The key point of a Local Search is when the search itself exhibits “explicit or implicit local intent”. So to take a basic example of when a customer is searching for “web design”, a Local Search would then be “web design in Essex”. Local Search can be particularly effective for local businesses because it lets advertisements be aimed specifically to search terms and specific locations defined by the user.
So what are Review Filters? Review Filters are set up on reviewing sites that monitor the reviews left by users; this is to minimise the amount of spam or biased reviews. It is designed to ensure that reviews are reliable, honest and representative of the business. This will help the business gain credit for a good service and increase the likelihood of securing more customers. However, in some cases businesses may hire people (or bots) to write good reviews or give unrepresentative feedback to make the business look better compared to rivals. Review filters will minimise this activity. There are different types of bot, in this case, the Review Filters we are talking about use algorithms that check:
- The extreme use of adjectives or profanity
- Overuse of keywords
- And inclusion of links
More sophisticated review filters use algorithms that encompasses criteria like: how many reviews a user has left, distribution of business types among all of a user’s reviews and frequency of reviews etc. Say, for example, Steve is new to an area and he is looking for a business that does video production. He would want to find a business that is local, reliable and has good feedback. So an easy solution to this is for Steve to perform a Local Search such as “video production in Essex” or possibly even more specifically “video production in Colchester”. From the results Steve may see their reviews left by previous customers which will give you a good idea if what to expect; now a Review Filter would ensure that these reviews are accurate, unbiased and honest, that way Steve can make an informed decision.
This all sounds very good right? Well there is one tiny problem, and that is that the algorithms can only find and detect certain patterns (such as those above) and it’s still no substitute for human rational and a bit of common sense. So carrying on with our example of Steve: he received a great service from his video production business and wrote them a very good review and gave the business a well deserved top rating, along comes the Review Filter and its algorithms interprets that Steve’s review included too many keywords and positive adjectives that make the business look good and decides that the review gets removed and not shown on the business’ page. Now clearly Steve is not a hired bot trying to boost the business’ ratings but a customer that is merely crediting the service he received, yet the business does not receive the credit due to the interpretation of the Review Filter’s algorithms. The algorithms behind Review Filters need a lot of work which why Review Filters are one of the most controversial topics in all of Local Search as it determines things like rankings in Google which play a major part in a business’ reputation, credibility and success. A recent study has shown that “80% of people have changed their minds about shopping with a business due to negative reviews”. This is an astonishing statistic to consider, especially with the wide availability of competition and rivalry faced by businesses.
Some of the other review filters are like the ones you see in comments after articles, posts and even product reviews such as those on Amazon. There you have the option to give an ‘up/down’ rating to a review left by a user which can give the review more credibility and feature higher up in the review listings. However with this empowerment comes the added risk of it being abused and gives rise to inflated ratings (back to square one); however we came back to the argument of common sense and human rationale. The review stays there, but the review has ratings that give or take credibility and gives more empowerment to the consumer as they have more information to their disposal to make an informed decision.
So where do we stand on Review Filters in Local Search? Personally I think that you are giving a lot of power and control over a business’ future success to a third party which can be taken advantage of. I still think a good ‘word of mouth’ reputation will always win because you are getting the information from a more ‘reliable’ source such as when you are recommended by your past clients. However in the case of the ever-growing online market, ‘word of mouth’ is replaced by digital reviews and it is important that they are reliable and unbiased. Review Filters are a good idea, but I think the algorithms need a lot more work and updating before Review Filters can actually be considered as a solution.