For more information, please visit our website.
A well-designed and fully integrated digital ordering solution can circumvent daily business challenges and deliver significant benefits to foodservice and hospitality operators.
Here are 10 key questions to ask when choosing a digital ordering solution:
1. Can the solutions be branded for your business?
-A configurable platform helps create a seamless brand experience.
2. Does the platform support multiple menus and pricing tiers?
-Flexibility can be critical to accommodating orders generated through Marketplaces like DoorDash and Uber Eats.
3. Can it support multiple ordering flows?
-Does it support standard flows such as pick-up and delivery, in addition to curbside, QR code ordering, catering, room service, table-service, and advanced ordering?
4. Is the platform scalable to meet your future growth?
-Can it handle an increase in order volumes, additional locations and/or brands, new service options, and third-party integrations?
5. Does it support multiple payment options?
-Can the platform accommodate gift cards? Loyalty and rewards programs? Meal plans? Split payments?
6. Is the platform omnichannel?
-Providing a consistent brand experience across web, mobile, and kiosk is important to ensure a positive guest experience.
7. What is the user experience like?
-How intuitive is the UI/UX for customers? Is the backend interface user-friendly for staff to manage menus, pricing, orders, discounts, and updates?
8. How well does it integrate with your existing systems?
-Is it easy to integrate with your POS system, payment, or meal plan solutions? Gift & loyalty? Can it support future extensibility through custom integrations?
9. Is the platform configurable?
-Can you configure or customize the user interface and/or features to align with your brand, menu, pricing, and promotions?
10. How does the platform support marketing and promotions?
-Does it offer tools for promotions, discounts, and push notifications? Does it support combos, recommendations, and upselling prompts to boost average check size?
Tacit, a leading digital ordering technology solution provider for Restaurants, Higher Education, Leisure and Entertainment, and Hospitality, is transforming how guests discover, view, order, and pay for their meals.
To learn more, visit https://tacitcorporation.com.
Assume that a customer wishes to buy a pair of sneakers (a highly popular product on online stores). They visit Zappos.com and see products!
Will the user browse through every single product? No.
Most users are not seasoned buyers. They become so engrossed in the images on the product listing page (PLP) that they completely forget to sort and filter (even if they did, all sorting and filtering functions would vanish with a little scroll down the page!).
More experienced users first choose their size (say, 10) to narrow down their choices. But, much to their surprise, there will be still products for the user to choose from.
And, that goes to show the importance of products on top of the list.
The better the top products, the greater the site&#;s sales. At the same time, if the products on top of the list do not appeal to users, they will not be willing to see the rest of the products.
Product sorting determines what products should be put on top or at the end of the list.
Therefore, a default sorting method is necessary for putting the &#;best&#; products on top of the list to maximize sales and create a better user experience.
A fundamental question arises here: How do you define a &#;better&#; product? What features does it have?
- Is being &#;better&#; necessarily the same as being more attractive or more viewed?
The answer is no. For instance, Lamborghini has very attractive products, but most people cannot afford them. So they shouldn&#;t be placed on top of the list because they may hamper the sale of other potentially saleable products (Unlike magazines which strive towards attractiveness, profit generation is the top priority in e-commerce).
- Is being &#;better&#; determined by sales figures?
Negative. A cheap piece of electronics may sell well, but will create a bad shopping experience for the user and may even damage the reputation of the online store.
- Are &#;better&#; products those with higher average customer reviews?
The answer is again an unqualified no. In fact, there are many products which have been rated and reviewed by few users or none at all, meaning that their average customer reviews are irrelevant.
It is obvious that the newest, the least expensive, or the most expensive product is not necessarily the best product. So Price: High to Low, Price: Low to High, and New Arrivals should not be used as the default sorting filters.
The concept of a &#;better&#; product is a complicated and multidimensional concept defined by different parameters including reviews, sales, user comments, novelty, profit margin, etc.
This paper proposes a sorting method that avoids the various complexities and uses a &#;simple&#; but &#;profound&#; concept to identify the best products.
The ratio of sales volume to the time a product is on sale is used as one of the most important parameters in the formulae used in recent innovative and advanced sorting methods.
These sorting methods suffer from a serious problem: Descending / Ascending Loop.
Meaning that products will be viewed less often and sell poorly if they are deep down the list. Poor sales will in return plunge them further down the list perpetuating the vicious cycle.
The opposite scenario may take place on top of the list. That is, the products placed high on the list are not necessarily good, just as the products at the bottom are not necessarily bad.
over results for Portable Power Bank! (Amazon.com)If new products are good, they should be put at the top. Placing them anywhere else will result in decreased sales. If new products are poorly-made, placing them on top of the list will damage the reputation of the business.
The problem gets more complicated when one realizes that product quality cannot be considered in black and white, and that a specific position should be determined for products of medium quality.
The intelligent sort is a new method that can be used by e-commerce companies to increase revenues and improve the user experience.
This sorting method can be used as different filters such as the most popular and the most favorite applied to the lists of products.
This formula benefits from a simple but deep concept at its core:
You will get efficient and thoughtful service from Keefam.
SPV = SALE PER VIEW = SALE / VIEW
SALE: the number of times a product has been sold
VIEW: the number of times the product has been viewed by users on the product listing page (not on the product detail page)
I.S. = SPV
In this method, it is no longer important to put the item at the top or bottom of the list. For instance, consider Product A and Product B:
Product A is at the top of the list, has been viewed 100 times, and has been purchased 20 times.
Product B is at the bottom of the list, has been viewed 5 times, and has been purchased 3 times.
Although A has sold more than B, B is the &#;better&#; product because it has a higher SPV.
Now you may wonder how to obtain the number of product views on the PLP.
It is possible to figure out how many rows of PLP users have seen by analyzing their scrolling patterns.
This simple code may give useful insight into the problem:
(this.scrollY+window.screen.height)/(document.body.scrollHeight)
It is also possible to figure out how many rows users have seen by determining what percentage of the total height of PLP is covered by the height of every row of products.
However, data do not have to be sent to the server every time users scroll the PLP. Rather, the number of viewed PLP rows (i.e. only a number) is sent to the server when the URL changes or when a browser tab is closed (or when a browser tab remains open for one hour).
The products viewed on the PLP can be determined by having the above-mentioned number and the matrix of the viewed list of products.
*Note: &#;Seeing&#; (done spontaneously) is different from &#;looking at&#; (done voluntarily); however, both are considered the same here for the sake of simplicity. In fact, distinguishing between the parts user see and the parts they look at something is impossible, even if sophisticated and expensive eye tracking equipment is embedded in every single user&#;s eyes. For more information, refer to &#;100 Things Every Designer Needs to Know About People&#;.
If a product is viewed more often and purchased less often, there might be something wrong with it. So the product gets a negative point every time it is viewed but not purchased.
Accordingly, is it necessary to count the number of times users view a series of PLPs without the intention of purchasing anything?
Assume that a user looking for sunglasses views a PLP of various products (e.g. shirts, pants, etc.). Is it necessary to count the views of irrelevant products?
The answer to both questions is negative.
An in-depth analysis of the problem indicates that the following points should be taken into account in SPV calculation for more accurate results:
A valid view is one that results in the purchase of a product. Unlike in-person purchases, many users visit online stores and just scroll pages without any purchase intention. So only views of products from the same category as the purchased product should be enumerated. In other words, if there are only two more pairs of sunglasses on the PLP, only the views of those two products should be counted. However, if all of the other products on the PLP are sunglasses, the view count of all viewed products is increased by one.
Additional Notes:
- If a user visits a product many times a day, the view count of the product will be increased only by one.
- The views of recommendation widgets are counted like PLPs; however, the view counts of PDPs (product detail pages) are not excluded from calculations.
SPV: main parameter (core parameter)
Yj: complementary parameters
Xi: customization parameters
Complementary parameters are optional, but using them will improve the accuracy of the results. The effects of user reviews are considered in these parameters. The general opinion of the public on a product should be added to or subtracted from the SPV; however, it should not be so large as to overshadow the main parameter.
The data employed in complementary parameters are extracted from different sections including average customer reviews, positive or negative reviews, recommendations for or against purchase, etc.
Since not all websites feature these sections and because people from different countries and cultures have different opinions of a specific product, this data cannot have equal effects on every business. Therefore, this study has refrained from putting a specific value or percentage on these parameters. However, the effects of critical reviews should be given more weight than those of positive reviews because even a single unsatisfactory product can have severe consequences for the company.
Personalizing parameters are used to convert sorts into recommendations, which are described in greater detail in the next section.
In the above formula, personalizing parameters are actually certain coefficients resulting in different sorts for different users. In other words, user behavior and log data can be analyzed to display the products that are both &#;good&#; and their personal favorites on top of other products.
For instance, if user behavior and log data show that they have been looking for only black T-shirts with no interest in other products, the sorting system can assign a higher priority to their favorite products &#;boosting&#; them higher than the other products of the same tier.
By default, the values of all personalizing parameters, including all features of a product (brand, color, material, size, etc.) are one. In the above instance, user experience can be improved by increasing the color coefficient of black products and displaying products that are both &#;good&#; and black higher on the list.
In another case, if a user&#;s size is medium, medium-sized products will be shown first even if no filter is applied.
A serious problem of e-commerce businesses is that they are uncertain about where to put new products on the list of favorites.
The online store will lose its appeal and customers if &#;good&#; new products are placed at the bottom of the list. The same is true if &#;bad&#; new products top the list.
One validation method consists of exposing products to users for a limited period to gauge public reception.
For this purpose, a value named SALE PER VIEW (SPV) is given to new products for a certain period and the product is put on top of the list for validation. (If the website traffic is high enough, a percentage of users can be selected as viewers instead of all users.) Also, the bottom products can be put through the same validation process for revalidation.
It should be noted that ignoring the SPV altogether and using the sales rate as the only parameter is problematic.
If only the sales rate is analyzed without considering the number of views, then, in the case of these two products, if:
A is a newly-added product temporarily put on the first row to obtain its sales rate (0.), and B is an old product with the same sales rate (0.) put on the sixth row, then:
The two products have the same sales rate. Should they be put next to each other on the list? The answer is no because A has achieved that sales rate because it has been on the first row (and viewed more often), and its sales rate will drop if it is put on the sixth row. This is exactly the type of problem that employing the SPV method can help solve.
After employing the I.S. to sort products and determine satisfactory and unsatisfactory ones, machine learning can be used to make the system more intelligent.
In fact, I.S. and machine learning can be combined to determine where on the list to put new products without validation.
Every product (e.g. a shirt) has certain specifications including price, color, brand, production year and season, size, material, pattern, etc. The &#;goodness&#; (popularity) of a newly-added product can be determined by analyzing the specifications of the &#;best&#; and &#;worst&#; (most and least popular) products.
However, the learning process requires valid data obtained from the best products, and that data cannot be obtained without using the I.S.
Want more information on Intelligent Sorting Solutions? Feel free to contact us.