Remember a time when there was no option to shop online and you had to spend hours at the mall to shop? That experience could be very emotional and stressful, so it led to the creation of a nerves-retaining profession – a progenitor of a personal shopper app – a shopping assistant. However, we can now buy almost everything with the click of a mouse or even with a tap of a smartphone – the mobile commerce industry is growing rapidly.
It still difficult to find exactly what you want among millions of items, even on progressive platforms such as Amazon or eBay. A search for the best deal takes a lot of time and this is where a personal shopper app comes in handy.
Reasons to Build a Personal Shopper App
A shopping assistant app is beneficial for both customers and businesses.
Typically, the services virtual assistants offer users include:
personal shopping services;
bookings and reservations;
ticket purchase and delivery;
bonuses, awards and last-minute gift options.
A lot of these apps cost nothing to download, but that is not the only benefit for users.
Pros for the Customers:
automatization saves time and money;
keeps all purchases organized;
tracks the orders from the app;
monitors price movements to make it possible to buy items for less money.
As for businesses, building a personal shopper app for iPhone or Android may be a great investment.
There Are Numerous Ways to Monetize Such Applications:
membership fees for brands and vendors;
Learm more about How Do Money Making Apps Profit
Shopping Assistant App Algorithm
The app can be powered by Data Intelligence or combine both AI and human elements. The artificial intelligence operates data intelligence apps – the specially developed algorithms make suggestions based on learning user behavior. There are no human consultations involved, which optimises the shopping experience quite a bit.
Such Apps Typically Include Tools to Make It Easier to Get the Best Deal:
Best price comparison;
coupons and discounts;
Human-based apps are commonly called an ‘online shopping assistant’. The real person is merged behind the interface, and they are who does all of the work: they make consultations and ensure that the offered solution is the best. Let’s take a closer look at the pros and cons of both groups of apps.
AI-based Shopper Assistants
As it was previously mentioned, AI-based applications rely on a set of machine learning algorithms. The apps are able to browse through hundreds of online catalogs and websites to find a required item many times faster than specially trained humans can. Also, AI-based advanced personalization should be marked: the algorithm searching the users’ data to learn the shopping trends, personal habits, and individual preferences. The collected information allows for making personalized suggestions and improving the accuracy of search results.
Here Are Some Ways to Integrate AI Technologies into Your Personal Shopping Service:
Natural-language processing makes it possible to communicate with an AI agent like you would communicate with a real person. It understands the simple human language, recognizes user requests and operates the required actions.
Image recognition is the alternative to the text search. Visual search processes uploaded photos and searches for similar items. If you like someone’s shoes and want to buy them for yourself, the program makes it easy to discover where you can purchase them (or similar shoes) by uploading the picture of the item.
Personalized recommendations are powered by machine learning as well. If you purchase the shoes you were looking for by using a visual search, the program can recommend to you other parts of the outfit that match. If you usually buy shoes from a specific brand, the app will show you their targeted offers that you might be interested in.
Price prediction algorithm learns of price drops or hikes to help you snag the best deal. You will be notified if the price on the item you are interested in will be reduced soon, so you can wait for a little to save some money on a purchase.
Mona is the most popular shopping assistant app, developed by a team of former Amazon employees. To make the application most helpful the team combined AI, big data, and human expertise.
As a description says,
Mona learns your style, the brands you love & your ideal price point! Mona offers you the best deals available in the styles your love. Tell Mona what you want and just like the best in-store personal shopper, she will help you find the perfect products.
The more you use Mona as your online shopping assistant, the better the recommendations you get. The app continuously learns about your style and preferences.
Mona searches through 1300+ brands in over 250+ online stores
Mona’s Co-Founder Orkun Atik admits that the biggest development challenge was taking hundreds of experiments and collecting a massive scope of data to improve the algorithms that make recommendations. To make them even more relevant, the app requests access to a user’s email to look at e-commerce receipts and learn preferences, style, size and other details. If a customer doesn’t like a certain brand, color or anything else, there is the option to provide additional feedback.
Mona’s users are pretty satisfied with the app. The only disadvantage they admit is that Mona cannot process payments. If you found the desired shoes and want to shop them, the app redirects you to the company’s website whose shoes you are buying. The team currently works on payments development.
Personal Shopper App Powered by Both Data Intelligence and Humans
The apps partly use AI and machine learning to automate certain tasks but generally rely on human agents. The mixed approach provides fast and effective services while keeping the human touch:
Fully customized services are provided. Users usually are looking for the sense of style and want to be pleasantly surprised when using a personal shopper app. The algorithms can hardly satisfy highly personalized requests, but an online consultant can.
Continuous learning from interactions with the client. The machines can learn, but only a real person can understand your personality to send you carefully selected recommendations.
Mezi is a great example of that combined approach implementation. It is specialized travel app, but despite on such narrow focus, it still offers personal shopper services.
The app learns users’ requests to capture the most important information by using natural language processing. Then the actual shopping experts come into play. They text with users and complete their requests. Mezi also uses AI to learn about customers’ preferences to complete future tasks faster.
This ‘combined approach’ requires heavy investment into machine learning and artificial intelligence technologies as well as trained shopping agents.
Building a Personal Shopping Assistant Is Relatively Easy
Basically, shopping assistants are similar to traditional mCommerce applications. They both include a user account, a shopping cart, payments, order tracking and shopping history. Here is an approximate estimate of feature development:
a simple chat – 40 hours;
a user account – 32 hours;
a shopping cart and payments – 32 hours;
order tracking – 16 hours;
a shopping history – 16 hours.
80-160 hours to implement all of the backend features listed.
Considering Other Options
Mobile development of a whole new application might seem like too much work for startups and small businesses. That is totally ok; there are some affordable options available. If you want to augment your existing app or website, a chatbot might be exactly what you are looking for.
Master of Code Global develops both personal shopping apps and chatbots. Have a project idea?
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