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Is your eCommerce team best in class?

So what does an eCommerce team do?

The eCommerce team role is to optimise the digital channel such that the maximum number of customers in the market are attracted to your website and of those the maximum number complete the transaction that you are offering to them. Optimisation requires the team to create and deliver the most effective traffic strategies, online advertising campaigns, product communications, customer engagement, sales execution, service execution and proposition fulfilment. We talk about a team as any eCommerce operation, unless it is very small, will require a range of resources, technical and commercial to ensure it is effective.

Smaller organisations might create this team from external support rather than internal resources and only have a single eCommerce manager.

Commercial imperative

From the outset it is critical that your eCommerce teams needs to have a central commercial objective that it's accountable to. As with any other channel, it cannot be held to account for things it cannot control, but it also should not be allowed to establish governing measures of success that are technical, or peripheral, as opposed to commercial.

Whilst a digital channel can measure a range of points of engagement for customers such as Facebook likes and Twitter followers, the channel should be held accountable to the same Profit and Loss criteria as any other. To do so, the commercial goals of the channel must be clearly identified and communicated by the eCommerce team to deliver one (or more) of the following objectives:

Our first principle for measurement in eCommerce is to ensure as best as you can that you are measuring the commercial outcome. A tweet, like or share is not a commercial outcome – it can lead to one, but it should not be taken or accepted as a proxy for one. A lead, on the other hand, is a commercial outcome because you will have tracking for the conversion of leads to sale that you can apply as a proxy for a commercial impact when measuring eCommerce effectiveness.

Our second principle is that, as far as you can, you should try to measure the same commercial outcomes across all channels, so your eCommerce team should be looking at price points, purchase values, margins, cost to serve, displacement savings (e.g. the cost saved in call handling through a successful online self-service) and profitability after the costs of capital employed. In doing so the eCommerce leadership team can articulate the current performance relative to other channels and the business as a whole is able to understand relative performance contribution and understand the value of competing investment requirements.

Whilst every business is different and will want to ensure its eCommerce operation fits into their overall measurement approach, some key measures should be consistent and this is especially important for conversion as this allows you to benchmark. However, we also think there is value to both the eCommerce team and to the business more widely of trying to measure two broad commercial outcomes:

Revenue Per User

Revenue Per User (RPU – also called revenue per visitor) is increasingly being proposed as the key commercial measure that eCommerce teams should refer to in judging the optimisation effectiveness of the channel, especially in retail sites where there is a relationship between the value of the products put into a basket and the likelihood that a customer will complete the purchase.

As a general point, in our experience business leaders do not pay attention to the vast majority of marketing measures. They focus more on the areas of primary concern that reflect the company's ability to generate more profit and faster growth than its competitors. This focus in eCommerce is potentially best addressed through adoption of RPU as a key measure where it could become one of the financial performance metrics of your site, and one of the primary objectives in any revenue performance optimisation strategy as it measures the money a website makes every time a customer enters an online store.  Increasing this is as important a commercial goal as increasing the number of customers who purchase.

We have argued for the supremacy of conversion as the primary measure and believe that it is such because it can act as a simple measure of effectiveness and it is translatable across businesses. But conversion doesn't measure value and this is where RPU comes in. One of the main goals will be to generate more revenue on your eCommerce site and it is very logical to think that increasing number of conversions (i.e. visitors who buy product on your site) will result in revenue growth.

In eCommerce, the conversion rate is the proportion of users that converted (i.e. made a purchase on the site, became a lead or completed any other central transaction). The commercial problem is that where the conversion is a completed sale the measures taken to increase the conversion rate can generate more transactions yet still produce lower overall revenues. This is for the reason that whilst transactions could increase the individual value of those transactions could reduce to such an extent that overall revenues remain static or reduce.  This can happen for a number of reasons but the main two are attracting customers who buy less of the product or service or who buy lower priced products or services.

Some companies think that the key for their revenue growth is to take measures to increase the average order value. For example, that is why product recommendation solutions that suggest to customers a particular product or service is so widely used. Average Order Value (AOV), or as it is sometimes referred to as an average ticket, is a measure representing the value of an average order within a period of time. It is simply calculated by dividing revenue by the number of conversions in a specific period of time. However it can also be true that there are no guarantees that an increase in AOV will translate into a proportional increase in revenue. This approach may encourage the sales of higher priced items, but the number of people who will make a purchase might go down, resulting in a decrease in overall sales.

Revenue Per User is a composite measure that combines conversion rate and average order value into a single number that represents the value of revenue per user within a period of time. It is calculated by dividing revenue by the number of users in a specific time period (normally to smooth out anomalies this should be at least a month).

RPU = (Revenue for time period)/(Users for time period)

It also represents an interaction between Conversion Rate and Average Order Value and it is one of the most reliable predictors of eCommerce revenue as you can also calculate it by multiplying the average order value in a time period by the conversion rate for the same time period.

RPU= (conversion rate for the time period expressed as a percentage of users who completed a transaction)*(average order value for the same time period)

It is therefore an early indicator of optimisation impact. If the RPU trend remains consistent or increases then the activities you are undertaking on the website are having a positive impact, if it starts to decline then it's a good early warning that you will need to adjust what you are doing. The baseline calculation in online retailing sites is therefore to understand the current value of the calculation.

R= number of conversions*average order value

We generally calculate this and other key measures on a moving monthly average basis to try and smooth out seasonal and other variables such as promotions or new product/service launches.

Customer segmentation

Once the eCommerce platform has the basics performing effectively, given the importance of securing consistent or even better RPU performance more sophisticated eCommerce teams will work closely with finance to build a commercial model to capture the lifetime value of users that transact on the website. 

Whilst important as a measure of overall trends on items such as basket value, the average customer is less interesting than the segments that make up the average. This is very different from consumer segmentation. Consumer segmentation should help you understand the market and in particular what part(s) of the market your eCommerce operation is targeting. This helps set a tone of voice, a retail engagement and shapes the copy and calls to action.

Customer segmentation looks at the part of the market that transacts with you and separates it out by size and or frequency of purchase. Knowing what the better than average customer looks like (i.e. the customer who spends more per transaction or who repeat purchases more often) enables better decision making on future sales and marketing strategies. As you understand this better so you can either encourage more customers to behave like this through better proposition execution or understand where they come from so that you can find more customers like this who currently do not buy from you.

Particularly in online business models, profitable growth comes from securing as many better than average customers than possible. Whilst the cost of acquisition may be higher, their collective profitability is far higher. We have developed a simple model for this (Figure 1) that can help you think through the overall customer mix and where the greatest value is to be had in an online market.

Ultimately, the outcome desired by the organisation is that it makes resource allocation decisions that optimize profitability and cash generation. For example if one segment is particularly significant, even if it is relatively less profitable, working to increase profitability of each transaction, even modestly, will make a significant impact on commercial performance.

There are considerable advantages of understanding the relative profitability of different customer segments these include the ability to:

All of these benefits help build a strong advantage for the early adopters and will enable them to resist the competitive challenge of later entrants or adopters of this approach.

There are, of course, issues with any such decision support tool. They require the same granularity of data for each customer segment identified. They will require data to be sourced from web analytical tools and from ERP/financial reporting systems. There will be some spreadsheet manipulation and the business will have to allocate analytical resources to establish a credible database. The key is to focus on the collective segmentation rather than individual data. In our experience, the best way of starting out is to start small and work up when the model has sufficient face validity. Your objective is to apply an 80/20 rule and we believe that for the majority of businesses this will provide better data than they have currently sourced.

This is the fifth extract from Leading Digital Strategy, a comprehensive book on the challenges of digital leadership, written by Professor Chris Bones and James Hammersley. Essential Retail is reflecting on the main themes from the publication, via a series of articles written by James.

Click here to read article one, 'Why are you so far behind in the digital revolution?

Click here to read article two, 'Customers are key to your growth: just ask them'

Click here to read article three, 'Make certain that your retail proposition sticks'

Click here to read article four, 'Are you wasting money on digital?'

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