E-commerce has become more competitive than ever. As an e-business owner, it's your responsibility to keep the company competitive and generate revenue.
So how to create a competitive edge when there are so many businesses offering the same product as you do? Especially, when you're operating on the online platform and have real-life communication touch points.
You might consider investing lots of time and money into creating an outstanding marketing strategy. The results might pay off, however, why not consider to develop a strategy that is more cost effective, yet very productive.
The e-world is now packed up with special tools and artificial intelligence (AI) systems that are revolutionizing the user experience. You can not achieve success without setting up an e-commerce recommendation engine on your web store. An e-commerce recommendation system is here to offer your business a bunch of benefits, one of which is saving money.
By investing in a good product recommender engine, you ensure higher sales rate, increased traffic, and better customer experience, yet you are also saving money.
What are the Types of Recommendation Systems
The recommendation engine is a system that filters data to create personalized suggestions and recommendations for each user based on their preferences. The data is collected individually for every visitor of the web page. The more data is collected for the user, the more relevant and personalized are the user specific recommendations.
The most common and widely used recommender system types are the followings:
1. Content-based filtering
This filtering focuses on creating highly personalized shopping experience. The algorithms of the recommender system collect data the websites visited, products added to the cart, products viewed and time spent on each product or page. Afterwards, the system creates a customer profile that is highly user-specific, so that the recommendations focus on a specific shopper only.
2. Collaborative filtering
This type of filtering is used when there is less information available about the specific shopper. The algorithms collect information such as ratings and past purchase data and match this data with other users that have similar preferences. So the recommender system uses other customers' actions and purchases to generate recommendations based on similar preferences of the users. The collaborative filtering helps to generate suggestions to people that the system has limited data about.
The smart technology behind e-commerce recommendation engines creates opportunities for e-commerce businesses to sell more, yet without additional expenses.
Keep reading to understand how exactly product recommendation systems help to save in the e-commerce.
How E-commerce Recommendations Can Boost Your Online Store's Profits
Personalized Recommendations and Increased Conversion Rates
The bottom line of setting up a recommender system is to create a personal experience for each user. That will get the website visitor to feel understood and valued by the store. Creating a proper product recommendation engine on your website might require some extra time and effort, however, it will pay off with high conversion rates.
Research shows that 63% of consumers are interested in personalized recommendations. Moreover, the majority of them are ready to share their date in exchange for a benefit of a personalized experience. Only a smart tool like a product recommendation engine can take seconds to rearrange the whole store to offer a single product that would fit the user's preferences. In the brick-and-mortar store salesperson is capable of aiding the shoppers so accurately and quickly.
Personal treatment of customers is highly appreciated and rewarded. Moreover, personalized recommendations will boost your company's conversion rates.
Conversion is extremely important for any business, as an indicator of how well your company is performing. Conversion rate measures what happens to a visitor gets to your web store and shows whether or not they are taking your desired actions. A properly working recommendation engine is supposed to convince the visitor to take an action and make a purchase, and personalization is a key factor for convincing the visitors to buy a specific product.
When the content of the website is shaped using data from previous and current shopping experience of the user, each and everyone will see a unique version of the web store that is tailored specifically to their preferences. When the system shows the visitor what they want and like, there are higher chances that the visitor will eventually buy the product.
Hence, the e-store will increase its revenue and conversion rates, while the customer will be satisfied and happy.
Recommender Systems Help to Achieve Customer Loyalty
Gaining loyal customers is what businesses strive for, both online and brick-and-mortar stores. Here's why loyal customers are so beneficial:
- They buy more on a regular basis, and hence, help to increase the cash flow.
- They are more forgiving to your mistakes, so there are less chances of losing a loyal customer even when you make a mistake.
- Loyal customers create a lot of word of mouth around your business, saving you marketing efforts.
- Such customers often leave you objective feedback, thus helping you with a continuous improvement of your brand.
- And most importantly, retention marketing, which is the root of obtaining loyal customers, is five times more effective than customer acquisition.
The above-mentioned factors will help the business save money, either directly or indirectly. That's why obtaining loyal customers is so beneficial for companies. Loyalty means understanding what the customers' wants from your brand. If you are able to give the shopper what he / she is looking for with the help of the product recommender, then you'll get a step closer to obtaining their loyalty.
So, personalized recommendations will help boost loyalty.
Costs of Having a Recommendation System
There are 2 options of recommendations systems: one option is to build it by yourself, and the other one is to buy an existing one.
If you choose to build your own recommendation engine, you will liberate yourself from relying to much on the company to which you outsource the service. However, you have to consider that it will take a longer time and might bring up a lot of technical difficulties, which will eventually result in more unexpected expenses. Having your recommendation engine means hiring analysts which will continuously analyze the data collected to improve the algorithms of the engine. This will also result in higher expenses.
The cost of the placement also depends on how many pages you want the engine to work on. E-commerce web pages usually set up the product recommendation systems on the homepage, cart page, and the product page. These pages work most effectively. But you can use the engine in the emails and other web pages too.
So, to understand the final cost of the engine to your business, you need to understand whether or not you want it to be in-house, and how many pages you want it to operate at.
Benefits of the E-commerce Recommender System
To sum everything up, let's look into the Benefits that e-commerce recommendation engine brings the company and the customers.
Benefits to the E-store
1. Increased revenue - the algorithms of product recommenders have gone through years of research and experiments until they got to the point they're at today. The system is now proven to drive sales and conversion rates of any e-commerce that is using a decent recommendation engine. In fact, Netflix stated that its own recommendation engine is worth $ 1 billion per year. At the same time, Amazon's 35% of revenue is generated by its recommendation engine. So two of the online giants credit a lot of their success to the recommendation engines they use.
2. Enriched analytics and database - having a big database on your store visitors' preferences used to be something any e-commerce business owner could only dream of. Thanks to the recommendation engines, this has become a reality. With their help, you get to have a big database of customer profiles, while the analytics puts them into groups based on their preferences.
3. Precise targeting - when you have all your customer segments grouped together upon their preferences, it's much easier to target each group with more precise marketing advertisements.
Benefits to the Customers
1. Personalization - the most obvious benefit we have already talked about.
2. Customer Satisfaction - personalized recommenders leave the customer more satisfied by their shopping experience on the website. When the shopper eventually finds what he / she looks for, they leave the web store much more satisfied and happy.
3. Productive shopping - another reason why customers like to shop with recommender systems, is that since the algorithms know their preferences, they sometimes pop up products and items that are surprisingly good. In the end, customers end up buying a product that is a new discovery, yet it's extremely useful to them.
The bottom line of saving money is creating an enjoyable and satisfying experience on your web store. Invest in a good product recommendation engine and save money for the future.