Selling over the Internet is a costly business. A survey by analysts IDC of 100 e-commerce leaders found these companies spent nearly US $6 million to build their e-commerce sites, US $8.6 million to promote them and US $4.3 million to maintain them. Given investments of this magnitude, companies establishing an Internet selling channel face an imperative to make that channel profitable.
Easier said than done however in the easy-come and very-easy-go Web world.
E-commerce retailers face a number of challenges unique to the Internet environment in making the channel profitable. The most critical of these challenges are;
- Ensuring the channel is open, all the time; as reports of income losses over the Christmas holiday period due to crashed Internet shopping sites demonstrate, ensuring the availability, and responsiveness of the Internet channel is not an easy task, yet is a fundamental requirement in e-commerce success. Not only do vendors face the likelihood of prospective customers not returning to the Internet site; they also face a negative impact on the corporate brand as a whole, increasing likelihood of purchasing through other channels.
- Improving the ratio of Web site visits to sales; Just as prospective customers won't stick around if a site is too slow, neither are they prepared to spend long trying to find the information needed to convert their interest into a sale. German broadcaster ORB, for example, found 5 clicks to be the maximum tolerance threshold, after which visitors would leave the site. E-commerce vendors must make sure that the information that leads to a sale is accessible within the minimum number of clicks.
- Increasing the likelihood of purchase through targeted promotions; the Internet comes into its own as a channel for interactive dialogue with customers and prospective customers because it provides so much information about them together with the opportunity to respond to that information immediately. Each visit leaves clues as to current and future buying intentions that can be acted upon whether the individual behind the clicks can be identified or not. Someone surfing pages on family cars at an automotive dealer site is a very suitable target for a referral ad to a child car-seat manufacturer's site, for example. However, whilst the data needed to reach these conclusions is available, it needs to be analysed effectively, and may need to be combined with external data sources such as demographics in order to be usable.
Channel integration
As if the issues involved in making the Internet channel profitable weren't enough, most companies face an even greater challenge - the Web is not their only channel. These companies (often referred to as "click and mortar" organisations) communicate with their customers through other channels including traditional retail outlets, call centres and mailing campaigns. Even companies whose principle selling channel is the Web (the so-called dot.coms) will use other channels to attract customers (email-shots, for example) and keep them (such as by offering a link to the call-centre from the Web site).
All of these different channels generate customer data that can be used to communicate on a more personal basis with that customer, on the basis that personalisation increases the likelihood of purchase. However, the types of data that each channel generates may be very different and it is only when all of the data relating to a customer can be combined together and analysed to form a holistic, meaningful picture that targeted marketing activities can be effective.
Channel integration is also important in maximising the return on investment in the channel infrastructure. A channel that is expensive to maintain should, in theory, be offered to profitable customers and not to e-window-shoppers. For example, a Web retailer might want to offer a call-centre service through its Web site, knowing that routing Web-site visitors to a call-centre increases the likelihood of a sale. However, call-centres are expensive to run and the retailer does not want to route Web site visitors to it who are not ready or likely to buy. By integrating and analysing information from different channels, the retailer can identify a profitable customer during their Web-site visit and switch them to the call-centre.
Although many organisations recognise that channel integration is vital to effective customer relationship management, few are yet able to do it in practice. Forrester Research reports that while 92% of the organisations it surveyed found a single, integrated view of the customer to be very important or critical, only 2% currently have a completely integrated view, with 10% achieving partial integration. Gartner Group estimates this at a slightly more optimistic 25%.
Integrating the e-supply chain
Behind the customer-facing channels discussed above lie the back-office systems such as order processing inventory, invoicing and procurement that are needed to complete a customer transaction. The efficiency of these systems has a direct impact on profitable customer relationship management - a customer whose Web order is not fulfilled on time, or who is billed twice for the same goods, is clearly less likely to return than one whose complete buying-to-receiving experience was positive. Again, integration issues abound and are exacerbated in many cases by the addition of the Internet channel since many click-and-mortar companies have separate inventory and billing systems in their retail outlets and dot-com channels.
E-supply chain integration is also important in successful cross-channel selling. For example, a clothing retailer might choose not to sell haute couture items over its Web channel since the likelihood of buying without trying on decreases as the price goes up. Instead, the retailer lists the stores, which stock the particular item. However, unless the store's inventory system is linked in to the Web-channel, the customer will not be able to see if the store has the item in stock.
Making e-commerce profitable through e-intelligence
E-intelligence gives organisations engaged in e-business the intelligence needed to make customer relationships more profitable.
SAS Institute's e-Intelligence solutions improve the three critical aspects of e-commerce profitability of;
- Personalisation
- The e-buying experience
- Web-site reliability and responsiveness
SAS Institute's e-Intelligence solutions provide intelligence on internet customer relationships that can be integrated within SAS Institute's CRM solutions to give a holistic picture of customers across all channels that is critical for successful customer relationship management.
CRM Software - Operational versus analytical
Customer relationship management refers to the process of establishing and maintaining personalised, profitable relationships with customers. That process involves both people and technology. Implementing a CRM strategy often requires a fundamental shift in the way a company operates, in order to put the customer at the centre of focus and unless the company addresses this essentially human issue, CRM strategies are destined to fail.
At the same time, there is a booming market of CRM software (predicted by industry analysts to grow by around 45% in 2000) that automates aspects of customer contact and helps companies identify individuals and groups to target with appropriate marketing activities.
The CRM software landscape divides into two principle categories - operational and analytical. Operational CRM software automates activities involving customer contact such as marketing campaigns, call-centre systems and lead-tracking systems. Operational CRM systems make the process of contacting prospective and existing customers more efficient. Analytical CRM software gives organisations the intelligence they need to implement CRM strategies that maximize the lifetime value of their customer base, increasing the profitability of each customer contact by making it targeted and personalised. Analysts such as the Meta Group recognise that, whilst most companies focus first on automating customer contact through operational CRM software, a CRM strategy will not succeed long-term if it does not include an analytical component. Without analytics, the company will not be able to make sense of and act upon the data it is collecting through the operational system.
Analytical CRM in the e-world
Irrespective of the channel, marketers are looking for answers to the same basic questions when they use analytical CRM tools, which can be broadly summarised as
- Associations:
What do people buy together? If you know which combinations of products and services your customers tend to purchase together (for example, annuities with mutual funds or fine cheese with quality wine), you can create more efficient marketing campaigns and increase your ROI. - Segmentation and Profiling:
What attributes do people share? What do customers who desert you for the competition have in common? Do you know which customer characteristics are most relevant to your profitability? Once you've identified groups of customers with similar characteristics, you can conduct special sales promotions and targeted mailings that take into account the particular needs of each group. - Behavioural:
What do people do, how do they react? What do your customers do when their bank account balance increases? When they miss three loan repayments? When they move into a new house? With the ability to anticipate likely behaviours given certain circumstances, an organisation can confidently determine how best to respond. - Exceptions:
Who does not fit the norm? Data mining techniques can alert an organisation to sudden drops in service usage, suspicious credit card purchases, and various other divergences from usual customer behavior. The ability to quickly identify such anomalies gives an organisation the chance to take appropriate action before the window of opportunity closes.
Equally irrespective of the channel, there are two basic technologies associated with analytical CRM;
- Data warehousing - a means of filtering, organising and storing the data around individual customers. A data warehouse is a critical foundation for channel integration since it is in the data warehouse that data around one customer is gathered together from different channels.
- Data mining - software tools that let you sift through large amounts of data to uncover data relationships, and to build models that predict customer behavior. There are many different techniques involved in data mining, and choosing a good data mining tool involves understanding which techniques will answer the questions that are relevant to the particular organisation. These questions will change with the company's business strategy. So- called "black-box" tools, where the techniques cannot be tailored to fit the individual need, are therefore likely, in the long-term, to deliver less return on the initial investment than data mining offerings with a wide range of techniques and a business process model that allows marketers and data mining experts to fit, and then re-fit, the data mining process to their company's needs.
There are two critical characteristics specific to CRM on the Web (or e-CRM as it is commonly called) that will affect the ability of existing analytical CRM tools to embrace this new channel and thereby facilitate channel integration. These are;
- Data volumes - the sheer amount of data generated by Web visits is huge, far in excess of any other channel. Many data mining tools will simply not be robust enough to cope with data volumes generated by Web sites with over 20 million hits a day.
- Real-time profiling - Traditional channels generally record only successful transactions - such as a sale, and therefore do not reveal much information about what causes initial interest to either result in, or not result in, a purchase. The Web provides an enormous amount of data on visitor behaviour that can be used to better target prospective customers. At the same time, however, the Web provides less information than other channels about specific individuals because unless a user registers at the site, or completes a purchase, it is generally not possible to identify them as an individual. Nevertheless, companies are able to target these individuals successfully by defining them in terms of group behaviour - in other words, in terms of the behavioural characteristics that they share with other individuals, some of whom will be known.
Data mining is used to explore clickstream data to allow companies to define the rules or parameters (a simplified example would be "If this person has previously visited these pages, then show them this referral ad") for their real-time Web profiling models. An important consideration for companies needing to update their profiling models regularly will be the ability of the data mining software to automatically feed the data mining results into the rules-based profiling software (as opposed to typing in by hand). The ability of the data mining software to integrate information from other channels is also very important since at the moment a visitor identifies themselves in some way on the Web site, it is possible to build a profile of them based on information from other channels.
Summary
Establishing and maintaining profitable relationships with customers means knowing as much about those existing and potential customers as possible. This Holy Grail of CRM can only be achieved with a CRM strategy that includes analytical CRM tools. The Web, as a new channel, brings its own dynamics. On the positive side, for marketers, it makes available a huge amount of valuable customer data that can be used to personalise relationships. On the other hand, Web customers share common behavioural traits of impatience and low brand loyalty. The use of e-intelligence tools that address the core issues of Web site performance, effective Web site design (often referred to as "stickiness") and customer profiling and targeting have a direct impact on e-commerce profitability. Ultimately, however, e-commerce initiatives will only be successful if they part of a CRM strategy that integrates, and can provide intelligence on, all of the organisation's customer contact channels. In the long-term, this will also imply the integration of all systems involved in the demand-to-supply chain.
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