What Are the Differences between Personalization in B2C and B2B?
Since younger generations have been nurtured by the aptness of b2b marketplaces, like Amazon’s recommendations and the simplicity of one-click buying, it is only natural they expect similar experiences from their business interactions. Rather than anticipating the same-style recommendations and garish marketing offers with freebies, B2B buyers expect B2C-like speed and simplicity. While personalization still has a place in B2B, its purposes are entirely different.
Personalization in B2C vs. personalization in B2B
Do B2B buyers want to receive personalized offers? What drives B2B buyers? What do they expect from recommendations?
In this article, we’ll answer those questions by differentiating between B2B and B2C personalization.
Origin of demand & decision-making
When talking about B2C ecommerce, we assume that individuals who purchase on the website are responsible for their shopping behavior: they act as their own decision-makers, operators, and accountants. All interaction between the web store and an individual happens within the confines of the available UX/UI, where the seller’s primary goal is to take a larger bite out of the buyer’s budget. Here, customers tend to be fickle and easily influenced by tempting marketing campaigns with extra perks such as free shipping.
If in B2C, customers are responsible for their own demand, and there is practically zero chance their expenditures would be controlled by a third party (unless it’s someone’s spouse, parent, or debt collector), in B2B, the situation is dramatically different.
The larger the business is, the fewer the chances are that the individual, who places an order with the web store, decides on behalf of the whole business entity. An operator typically has minor responsibilities that don’t go beyond order registration. In bigger companies, multiple parties participate in the procurement process that usually spans over a long period and involves several approval stages.
Moreover, the origins of demand in B2B can differ depending on the type of industry. For example, in wholesale, the demand is based on a sales plan (or sales forecast), where businesses only buy what they expect to sell later. Because of such mathematical precision, the people involved in procurement in B2B do not respond to discounts in the same way as individual end-users in B2C. Discounts (when dealt with on the spot during order assembly) can rarely prompt wholesalers into a spontaneous purchase, even though a price decrease can increase the wholesaler’s margins. For example, suppose a web store offers discounts on items that are not in the wholesaler’s sales plan (or procurement order). In that case, the company’s representative will just ignore the offer, no matter how generous. The reason for rejecting a lucrative discount is self-explanatory – companies typically do not allow employees to change the sales plan (without the approval of several senior managers). The purchasing control is even stricter for manufacturers: there is a production plan and procurement schedule to abide by.
Behavioral model & attitude towards shopping
If in B2C, shopping is synonymous with entertainment, in B2B, shopping is someone’s job and a responsibility. People behind the order process in B2B don’t expect “fun” from the shopping experience but rather speed and accuracy, which can be accomplished via features like asp net shopping cart open source.Therefore the goal of a B2B web store owner is to help customers complete orders as quickly and accurately as possible. With that said, it doesn’t mean that there is no place for personalization in B2B – there certainly is, but it needs to fulfill other functions.
Sometimes, however, we can observe B2C-like behavioral patterns in B2B. Such behavior is prevalent in situations where a company buys products for internal consumption (as opposed to production or resale). For instance, if an office manager shops for coffee and cookies for employees, they are usually driven by either personal preferences (within the assigned budget constraints) or the preferences of people who will consume the procured products. However, situations like these do not define the B2B context (and are instead an exception to the rule).
Account structure & account management
A user’s account in B2C is essentially the business account. On the contrary, a business account in B2B doesn’t belong to an individual entity but to the entire organization. Because companies do not operate as abstract entities but are managed by individuals, a business account comprises multiple users who work on their behalf. Regardless of the granularity of a company’s account structure, what the web store owners essentially deal with is the organization as a whole and not its individual users. For example, the prices that users see are not personalized per user but the company they represent (and are typically conditioned by the contractual obligations between the parties).
Recommendation engine & seller goals
In B2C, a seller’s goal is to maximize profits within a specific timeframe by nudging a buyer to spend more. Thus, the goal of a recommendation engine is to show the buyer as many relevant products as possible so that they pick up more items even if buying them was not their initial intention.
In B2B, a seller’s goal is to help a user accurately assemble an order faster. Anything that hinders the user’s shopping journey, such as unnecessary distractions or irrelevant recommendations, will not only annoy the user but might eventually steer them off to a competitor. If we look at personalization at this angle, then it becomes clear that the goal of a recommendation engine is not to nudge users to spend more but to prevent errors and reduce the time it takes to make a purchase. Consider an example: Since assembly of an air conditioning system involves choosing dozens of parts that can either fit/work with each other or not (i.e., ventilation ducts have different types of slots, surface areas, and so on), a recommendation engine should suggest items based on their complementarity (compatibility).
In B2B, recommendations can also be based on customers’ previous orders. For example, if a retailer buys products from a wholesaler regularly, chances are they purchase the same products every other month. By analyzing orders from previous periods, web store owners can forecast and recommend products that might be ordered again. Relatively big wholesalers (with the right expertise and extensive data) have more capabilities to predict the retailers’ demand than the retailers themselves. Such forecasting capabilities can help smaller players efficiently and accurately assemble their orders.
Order management & processing
Because in B2C, users act as decision-maker, operators, and accountants, the information they possess is exhaustive.
In B2B, the user roles are granular and restricted by their job responsibilities. For example, an operator’s responsibilities are purely mechanical: an operator assembles an order following the procurement plan from the ERP, puts items in a shopping basket, and requests a quote (if necessary). Next, a procurement manager checks the shopping basket for accuracy and either approves the assembly or rejects it. If the procurement manager approves the order, it gets transferred to the next in line, usually a financier or controller, for additional checkup and approval. Finally, someone is eligible to click on ‘check out’ and proceed with invoices and payments. Depending on the context, there could be other roles: an accountant, who, by logging into the system, can see nothing but the list of invoices.
A B2B ecommerce platform needs to account for such granularity in responsibilities and complexity in approval management so that an order can’t move through the approval workflow without the explicit consent of responsible individuals. The personalization from this angle should address role-based access levels and permissions.
A note on predictive analytics
B2B ecommerce platforms typically lack predictive analytical engines because artificial intelligence (AI) and machine learning (ML) are separate fields with their own experts. The more data the engine processes, the better the engine is trained. When in doubt, choose an ML company that works with other businesses in your industry because this way, it will have access to vast amounts of relevant data.
Although Virto Commerce doesn’t have its own ML/AI engine, the platform is API-based and can easily integrate with third party software to make any engine deliver its analytical data to the platform.
Whatever relevant (industry-specific) engine you find, Virto Commerce, ultimate best open source b2b ecommerce platform, can help you process its data, translate, and deliver its predictions to the platform in the form of your choice. For example, the engine’s data can be presented in the form of an order draft or a separate panel with recommendations on the side of the screen sorted by their potential relevance to a customer or frequency of previous purchases.
Why is Virto Commerce the B2B ecommerce platform of choice for accurate and efficient B2B personalization?
Virto Commerce B2B ecommerce platform supports a flexible account structure that can differentiate between varying business units and their users working under different access levels and permissions. Virto Commerce considers the flexibility of business account management as its top priority because, without the relevant account structure, it’s virtually impossible to achieve the desired level of personalization in B2B.
In Virto Commerce, you can set up as many permission (and access) levels as you want, directly from the platform’s front end.
Thanks to its robust Pricing Module, Virto Commerce can deliver personalized price lists to specific business units (either departments, branches, or whole organizations).
Virtual catalogs guarantee that your customers only see contractually agreed-on products, which takes personalization even further.
Being a headless ecommerce platform, Virto Commerce allows you to create as many front ends (with different customer experiences) as you want.
Virto Commerce helped a beer brewing company implement an order prediction form based on the analysis of the previous orders. The form is essentially a reorder request with the most probable items and their quantities. Customers can simply click on reorder or edit the form by adding or deleting items.
Because of the legal requirements, the company also asked Virto Commerce to restrict the operators’ access to specific prices. When operators log into their accounts, they can see the contents and quantities of customers’ orders, but not the prices per item.
De Klok Dranken, another Virto customer and a leading company in the beverage and food industry in the Netherlands, still has customers who prefer doing business the old way. Instead of placing orders themselves, they prefer to talk them over with a company’s representative. Because of the customers’ reluctance to fill out order forms online, De Klok handles their accounts through its sales managers. When sales reps visit customers on-site, they log into customers’ accounts to access their specific and contractually agreed on prices and place orders on customers’ behalf.