Different examples of sales funnels in ecommerce
Digital Strategy / Omnichannel Analysis
Sales Funnel Analytics
A sales funnel is a marketing model that illustrates a theoretical path your client would make from getting in touch, exploring a product to completing a transaction. This is an extremely simplified model and describe the phased of an ideal consumer path, a Consumer Journey.
In reality, the path is mostly nonlinearly - users can return to the previous stages, loop multiple times, leave the page and come back at a different time or lose a desire at all. However, a sales funnel is an indispensable element of any marketing concept, because of unified and visual components, which allows finding stages with the highest drop-offs and eliminate them.
A funnel may vary for each type of business. In addition, steps and phases may change during its development to optimize the measurements. Often times tools like Tableau, Looker, PowerBI or Data Studio will be used in order to provide the visualization for the analysis purposes. Each visualization tool needs to be aligned with this requirement and make the funnel analysis as flexible as possible.
Few examples where funnels are useful for analysis:
- “From the first interaction to closing” - shows the stages between the first interaction, assisting interactions up to the sale itself in a particular time period
- “Sales only” - displays only the transaction cycle. May have quantitative or qualitative indicators. For example, consider the number of visitors who have moved to the next stage or evaluate the quality of staff at a particular stage.
- “Post-sale service” - includes the interaction that follows after the sale (delivery, warranty, assembly, etc.).
- “Cross-sales and additional sales” - after passing through the sales process, the funnel continues with intersecting and additional sales (upselling, post-launch services).
Like mentioned earlier, a sales funnel may not be universal for all forms of business. It varies depending on the specifics of the product, sales patterns, distribution channels, and business size.
This is how a funnel in a retail segment might look like:
- Internet advertising / Newsletter / Visiting a showroom
- Interaction with the website / Retargeting
- Product view + Adding product to the cart
- Refunds / Returns
Rules to follow
- If you use a multi-channel prospecting model, the funnel needs to be made for each of them separately. This is important to understand the marketing performance for each channel separately since the consumer’s path in those cases will always be different, even if the main process remains the same steps.
- Funnel stages may coincide with business processes or may combine several in one funnel step. For example, on marketing funnels, the checkout steps can be combined.
- Clearly outline the boundaries of each step. This will clarify the Customer Journey and make drop-off analysis much easier (e.g. is cart page already a part of the checkout process or not).
- Separate funnels that may include specific behavior from the general process, e.g. in case of a cold commercial offer to a selected audience vs. an individual offer for a specific customer, since the success rate may be misleading for marketing analysis.
- Note that the consumer can move along the funnel non-linearly: both forward and backward.
- Tiring to develop a “perfect” funnel immediately in the beginning. Create a simple, working diagram first and as soon as you get the first experience, you can begin to improve it.
- Making the funnel to granular. Smaller steps may get more information for a particular phase but are not recommended for the process optimization in the first step. Only those activities that carry a semantic load should be monitored.
- Reporting only on a session basis and not the number of leads, e.g. if suddenly your lead walks the same process multiple times, the number of sessions in the first phases increases, which has a negative impact on the conversion rate.
- Only comparing total numbers between the steps on a funnel without period-over-period comparison in each step separately, e.g. sales number may look good in comparison to a number of leads, but in PoP comparison may show a decrease to the previous month or be lower in a year-over-year comparison for the same time period.