With this in mind, many of these same business leaders have also accepted that social media should be leveraged, and have thus established a presence on the most-used social networking sites. Many have even gone the extra mile and actually engage with their customers on these sites.
But, what is often most important is what customers do and say online.
The beautiful product photos, positive reviews, and check-ins that customers post spread awareness about the businesses, products, and services that they use and hopefully like.
What they also do is leave a permanent record of a positive (or negative) interaction that a customer had with a brand.
As you know, if it is posted on the Internet, it can possibly live on forever.
What we don’t often think about is that these posts can lead to future sales by helping recommendations engines provide more targeted and accurate suggestions to future customers.
What is a recommendation engine?
In the context of what I am referring to, it is an information filtering system that helps a business recommend items to customers that they might be interested in. For additional information, Wikipedia has a good explanation.
If you want to see an example of a business effectively using a recommendation engine to help its customers find products, visit Amazon.com. The Amazon.com recommendation engine uses a combination of several input data, including past purchases, product ratings, and social media data.
Social Networking Sites Offer Suggestions
Several social networking sites understand that the data that they collect can be very useful and have harnessed it to offer recommendations to users directly within the site.
Foursquare is a great example.
In his book, “Mobile Influence: The New Power of the Consumer,” Chuck Martin describes how Foursquare is using its data to offer better suggestions to its users.
In the book, Eric Friedman, director of sales and revenue operations at Foursquare, states, “From the very first check-in, we get smarter at what we recommend. If you check in to a series of places, we will make a better guess at what you are looking for. If you love small coffee shops and you go to a city and type in ‘coffee shop,’ guess what we are going to recommend? A small, independent coffee shop. If you are a guy that loves a big coffee house and you go to a different city or country and type in ‘coffee,’ we are going to give you recommendations based on your history. If we were friends on Foursquare and I was in downtown Boston and I saw Chuck had been to a cheeseburger place five times, that is a great signal for me to go to the same place for lunch because I know Chuck and he knows good cheeseburger places and I like Chuck.”
The book goes on to explain other ways that Foursquare is using its app and the data it collects to give its users targeted and relevant suggestions based on their location, past check in history, and the check in history of the people who they are connected to.
If you want another example, check out Yelp.
As you are probably aware, Yelp is an online review site that allows users to review businesses that have a brick-and-mortar location. This data can be used directly within the site to find a specific type of business based on its location and the reviews that it gets from Yelp’s users.
Yelp has an algorithm that that helps surface the most trusted reviews from the most reliable sources.
It is also noteworthy that Yelp reviews often show up in the results that users get when they search for information on Google.
Every Post on a Social Networking Site Could Potentially Be a Source of Data
The examples that I gave demonstrated how social media can be used to help users find businesses based on data collected within the social networking site itself.
However, everything that users post on social networking sites can be used by a third party to help consumers make purchase decisions. (As mentioned, Yelp reviews show up in Google SERPs.)
To illustrate this further, think about all the photos of the delicious meals that users post on Instagram.
Knowing that people often post photos of their food, the app MyFab5 encourages users to use these Instagram photos to rank the five best places for a specific type of food in a specific city.
The concept is rather simple (i.e., use food photos to rank the five best places for a specific type of food in a specific city.) The app then uses an algorithm to surface the best places to get a specific type of food based on users rankings. For example, according to MyFab5, here is a list of the best places for burgers in Minneapolis, Minnesota.
While this data again leads back to a brick-and-mortar location, it shows that anything that users post is fair game.
Given the vast amount of data out there, there will be other businesses that will harness other types of user-generated content to help make recommendations to other consumers based on hashtags, keywords, geotags, or other data that are included in posts on social networking sites.
Therefore, it is important that businesses find ways to ensure that these recommendation engines find more positive posts than negative ones.
As I have pointed out, the product photos, reviews, check-ins, and other posts on social networking sites not only work to influence the people who are connected to the users who create the content, but they also can have a larger impact on future sales when they are used to fuel recommendation engines.
So what can businesses do to help encourage customers to create user-generated content that displays the brand in a positive light?
The answer to that question depends on the situation.
However, the most important thing is to provide great products and services to customers.
Providing excellent customer service is also key.
In the end, businesses not only want customers to use their products and services, but they want the experience that they have with the brand to be positive. So positive that customers can’t help but share the love of the brand online.
Because what is posted online can live on forever and we can’t predict how other businesses will use that data in the future.