7.1.1 DEL ACCESO Y TRANSITO EN LAS INSTALACIONES.

Figure 4.3: User Growth: January 2017 - April 2018 4.2.2.3 ARPU and ARPA

The average revenue per user (ARPU) has varied between 30 and 41 USD. The change in ARPU is due to price difference between monthly and annual users and the ratio of annual to monthly users vary over time. Differences in ARPU can also be explained by the discount that some customers get if they purchase subscriptions for larger quantities of users. The ARPU has not increased significantly over time.

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Figure 4.4: Average revenue per user: January 2017 - April 2018

The average revenue per account (ARPA) has improved over the last 16 months. This is due to a shift in focus to targeting larger customers with more users to increase the annual contract value (ACV) of every deal. The ARPA for April 2018 is 226 USD and has increased from 120 in January 2017.

Figure 4.5: Average revenue per account: January 2017 - April 2018

4.2.2.4 Churn

Churn occurs when customers stop paying for their subscriptions. As discussed in section 3.2.2, the monthly simple churn rate does not perfectly reflect reality for a SaaS-business with high MRR growth. This is because churn rate is calculated based on the incoming MRR for a particular month, while churn is a lagging indicator from users in the past. The simple churn rate for GetAccept varies between 0,5 and 2,5 % over time with no significant improvement.

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Figure 4.6: Monthly Simple MRR churn rate: January 2017 - April 2018

As GetAccept have users on both annual and monthly contracts, not all users can churn each month since they are tied up on annual subscriptions. Therefore, the available churn ratio depends on the ratio of annual to monthly users.

Figure 4.7: Monthly Available MRR churn rate: January 2017 - April 2018

Churn Cohort Analysis As discussed in section 3.3.2, cohort analysis is a great way of understanding the real churn in the business. It compare segments of users to each other in order to find patterns in user retention. Since GetAccept have users on either monthly or annual subscriptions, the two types of subscriptions are separated into one cohort analysis for monthly and one for annual users.

The cohort analysis of monthly users shows that the churn is relatively high. The net MRR retention rate for most cohorts is below 100% which means that churn is larger than up-sell for those customers. There is a large difference between different cohorts of users. The six-month net MRR retention rate varies between 60 and 109% with an average of

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86%. The retention rate has improved somewhat over time. However, it is not a linear improvement and it is unclear if the improvement is statistical significant.

Figure 4.8: Net MRR Retention Rate (Monthly Users): January 2016 - April 2018 The cohort analysis for annual users shows that the net MRR retention rate for annual users is significantly higher than for monthly users. When users are on annual subscrip- tions, they can’t churn until month 13. In month 13, however, the net MRR retention rate doesn’t drop below 100% in any of the cohorts. This means that annual users have net negative churn. Net negative churn’s exponential impact on growth is discussed in section 3.2.2.2. However, it is worth noting that only three cohorts have yet come to month 13 which is when its users can start churning. After nine months, the average cohort retention rate is 121%. The strong 9 months retention rate indicates that the 13-month retention rate will be above 100% thus reaching net negative churn for rest of the cohorts as well.

Figure 4.9: Net MRR Retention Rate (Annual Users): January 2016 - April 2018 Averages in cohort analyses can be misleading since the number of users in each cohort varies greatly. Thus, it can be beneficial to look at the weighted average net MRR retention rate (WAR-rate). The WAR-rate combines the cohorts and weigh them on number of users in the cohort. The graph shown can be seen as a representation of how an average user (or one dollar in MRR) grow over time.

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In month 13 there is churn, but the accumulated up-sell during twelve months is larger.

Figure 4.10: Weighted Average Net MRR Retention Rate (Annual Users): January 2016 - April 2018

The WAR-rate of monthly users is significantly lower than that for annual users. The net positive churn is linear which indicates that there is not one particular month in the customer journey where users stop subscribing to the product.

Figure 4.11: Weighted Average Net MRR Retention Rate (Monthly Users): January 2016 - April 2018

4.2.2.5 Engagement metrics

For GetAccept, a monthly active user (MAU) us defined as a user who has logged in or used the product’s API at least three times in a particular month. The engagement ratio is defined as the percentage of total paying users that are active. Below is a graph of the engagement ratio plotted over time. As shown in the graph, the engagement ratio has improved significantly during the fall of 2017 until the start of 2018 and has since then

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been flat. Naturally, the engagement in the product is lower during the summer since less people work then.

Figure 4.12: Engagement Ratio: June 2017 - April 2018

From the cohort analysis of the engagement ratio, it is clear that users’ engagement their first month of subscribing has been a key driver in the overall increased engagement ratio. The six-month engagement rate has not been improved however. Users have become more active the first three month of subscribing. The user engagement ratio three months and more into the users’ subscription has not been improved.

Figure 4.13: Engagement Ratio Cohort: June 2017 - April 2018