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Ensure Customer Satisfaction & Boost Conversions With Cocosearch

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Conversions With Cocolabs

 

A reliable provider, a satisfied customer

 

Ecommerce sites set up their search engines so as to offer the buyer the result that is closest to what was searched for: I want a red balloon, the platform offers me red balloons. This outcome is the best-case scenario, as it gives the greatest probability of a successful transaction. Once the buyer finds what he’s been searching for and proceeds to payment, it is taken for granted that the merchandise will be delivered – because ecommerce sites have total control over the purchase process.

 

Service marketplaces, on the other hand, have to deal with suppliers of widely varying reliability. They are faced with a good deal of flexibility as to time and place of service delivery – i.e., the service provider can only deliver the service at a certain time and in a certain place. On top of this, there is the uncertain reliability brought about by the diversity of represented suppliers – micro businesses, solo entrepreneurs, individuals – of whose ability and/or desire to honor demand one cannot be sure.

 

In order to adapt to the constraints and meet the special requirements of service marketplaces, Cocolabs have tested a variety of search algorithms on different platforms. Below Roobykon Software team made a retrospective overview of how the Cocosearch algorithm evolved over time to deliver the most relevant search results to customers of the service marketplaces that use the Cocorico engine.

 

1st generation of the algorithm

 

The first generation of the algorithm did not address the offer reliability problem – the problem of how likely the person offering a service is to actually be willing/able to provide it. Instead it applied the traditional methodology of ecommerce sites directly to marketplaces. Accordingly, results were ranked by distance from the searched-for location, and according to the dates declared as available (the goal being to provide the closest result to what had been searched for). A few hundred transactions were processed before a major problem was identified concerning the rate at which suppliers were refusing to honor service requests.

 

2nd generation of the algorithm

 

After discovering how many refusals there had been from providers to fulfill the services they’d advertized, the Cocorico team upgraded its search algorithm so as to promote results whose availability dates had been explicitly declared by the provider. Marketplaces based on the Cocorico engine generally consider the provider to be always available by default – but if the provider wants to improve its ranking in search results, it can explicitly declare its periods of availability and unavailability. This change delivered considerable improvements, but still left a lot of room for further advancements.

 

3rd generation of the algorithm (Cocosearch)

 

We now clearly understand that the best result for the customer, the provider and the platform is whichever is most likely to lead to a successful transaction workflow. We also believe that the greatest obstacle to the conclusion of a successful transaction is a refusal or lack of response from the provider. With this in mind, the Cocolabs team reoriented its search algorithm to pay greater attention to the historical behaviour of providers.

 

The principles of the algorithm are based on the following elements:

Location

A search by location returns a series of results close to the location specified. This list is divided into two parts:

  • Exact results - The exact results are those whose location (street, zip code, city, county, region or country) exactly correspond to the specified place.
  • Close results - The second part of the list comprises all those results close to the specified place.

 

Availability

During a search by date, the two lists of results, divided by location (see above), are further divided, each again into two groups:

  • Proven availability - Listings whose dates of proven availability match the dates of the search – these appear first.
  • Undetermined availability - Below appear listings whose dates of availability are unknown.

 

Platform rating

To each of the listings within the four groups described above, which appear following a search by date and location, a rating is assigned based on the following rules:

  • Listing completeness (weight X) - The degree of listing completeness takes into account the amount of information given by the provider about the listing: whether or not details are specified or a title has been entered, if the description contains more than 250 characters, whether a price is specified, whether the number of uploaded images is greater than the required minimum etc…
  • Profile completeness (weight X) - The profile completeness takes into account the amount of information entered by the provider, including whether its description of itself contains more than 250 characters, and if there are more images uploaded than the required minimum.
  • Profile rating (weight X) - Takes into account the ratings that the supplier has received. Results sorting is designed first of all to rank down poor providers, rather than to promote good ones. This approach is based on the idea that a good service is the expected standard, and so poor service has to be heavily penalized. The engine takes into account the average score of the provider, which is weighted by how many individual ratings it is made up of.
  • Date of most recent calendar update (weight X) - The fact that a supplier’s calendar is regularly updated is correlated to a greater willingness to respond to requests and provide the service advertized – so, having a more recently-updated calendar will improve a user’s search rating.
  • Number of completed services during the last 30 days (weight X) - A higher frequency of recent service completion suggest a willingness to respond to requests and to provide the services offered. Ratings are adjusted based on the number of bookings paid for and not cancelled within the past 30 days.
  • Message response rate of the provider (weight X) - To calculate the rate of response, Cocosearch divides the number of messages sent by the provider by the total number he has received, improving the rating of suppliers with a higher response rate.
  • Acceptance rate (weight X) - The acceptance rate of the provider is the percentage of claims which he has accepted, whether or not the booking was completed.
  • Transactions success rate (weight X) - The number of successful transactions is calculated based on the number of bookings whose transfer has been authorized in relation to the total number of bookings.
  • Response time (weight X) - Taking into account the response time of the provider allows Cocosearch to favor those who are the most responsive to customer requests. The response time rating is based on the time between the last message received and the response made to it for each of the supplier’s discussion threads. If no response has been made by the customer, this time is not taken into account. The rating is adjusted based on the average time of response.
  • ‘Certified’ status (weight X) - This status can be assigned to listings by an administrator, for example after certain documents are provided by the supplier. Possession of ‘certified’ status helps the listings attain a better ranking in search results.
  • Newcomer bonus (weight X) - To welcome new providers to the marketplace and to give them a chance, a newcomer bonus is assigned to all listings which were added less than 30 days ago.
  • Random bonus (weight X) - In order to prevent listings’ immobility within search results, and to encourage providers which have been unable to obtain a favorable ranking, a random bonus is awarded to 5% of site listings. The receivers of this bonus are changed each day at random. The weight of the bonus also changes at random.

 

TO SUM UP

 

When managing a platform focused on the sharing economy, the major goal is to ensure customer satisfaction and maximize the conversion rate – and further, to make the enterprise profitable. The main difficulty of this market is in the fact that no collaborative platform is able to guarantee the reliability of every listing placed on it – because the providers are usually individuals, or professionals acting in a private capacity. The latter, who do their main business outside of the platform, do not show either the dedication or the involvement of a company whose ecommerce business is its sole or main activity. The major risk is that the customer faces repeated refusals, or even worse, an absence of responses.

 

The listings of unreliable providers must be identified and ranked down in search results. Such unreliability appears according to the provider’s situation – the availability of the things he is offering as part of his service, and his need to make money from them. For example, if I am planning to take a trip in four months’ time, I might offer my car for rental (and therefore also do without it) during the period between now and then, in order to save money for my travels. But there’s a strong chance that a potential customer contacting me in six months’ time about rental of this same car will get a refusal or just no response, either because I have not updated my listing, or I simply because I’ve stopped visiting the site. This experience happens again and again for a great number of customers of collaborative marketplaces; it should be avoided at all costs, no matter what the stage of the platform’s lifecycle.

 

For platforms to succeed it is critical that they don’t look at the sharing economy as we have, to date, been accustomed to looking at the rest of the web. The participatory economy breaks the rules. It revolutionizes distribution networks and reshapes consumer psychology. Now, the customer is no longer looking for a cheaper place to buy for the product which he noticed in the store but found too expensive; rather, he’s after something that ‘feels right’. Customer satisfaction is subjective, yet it will be the holy grail of the search engines used in future service marketplaces.

 

The essence of Cocoseach can be found in the reasoning behind the following conundrum: consumption habits have changed, customer expectations have changed; the way of providing services must change so as to favour the results that will lead to a successful transaction.

 

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