Predictive model

What Tasks Will Be Performed

During the analysis, we created a model which predicts whether a person will subscribe or not. It makes a correct prediction in almost 91% of cases which is considered to be very precise.

  • You should NOT use it: for defining people who will agree to subscribe. It works bad.
  • You should use it: for defining people who will refuse to subscribe.

Although the model does not fit for identifying subscribers, this drawback does not seem to be important for your company. If the company does not spend resources on attracting clients, but they still subscribe to the service, you will loose nothing.

In contrast, from the point of view of monetary gains and losses, it will be much more crucial to waste resources on working with clients who will not subscribe anyway. In fact, this model will help to save company`s money, since it defines non-subscribers in 97% of cases.

So, our goal is not only to improve your subscription rates, but also to save you from wasting money on non-profitable actions.

Describing Subscription Process

From the tree, we can track the subscription process in its initial stages. Here is a description of several combinations of factors which can lead to the positive outcome: a person will subscribe for the service.

  • At the first stage, the outcome of the campaign depends on the duration of the last contact with a person. If it lasted more than 8.4 seconds, then in 43% of cases a person would agree to subscribe
    • If the duration was even more than 14 seconds, a person would agree to subscribe in 59% of cases.
    • If not, but a person subscribed during the last campaign, they would subscribe in this one in 83% of cases.
  • If the last call`s duration was less than 8.4 seconds, but more than 2.7 seconds, given that people subscribed during previous campaign, they would subscribe again in 73% of cases.
  • If both the of the last call was less than 8.4 seconds and a person did not subscribe during previous campaign, if a person was contacted in March, September, October or December with call duration more than 3.1 seconds, 58% of people would subscribe.

To sum up, if your company will apply one of presented scenarios to the work, it will be possible to increase subscription rates both in this and in future campaigns.

Looking at Individual Subscription Cases

Now, let`s have a closer look at the way how the combinations of factors can influence the outcome of the subscription process for indivisual cases.Here we have two cases: for subsription and refusal to subscribe. Green bars show that the given factor increase the chances that a person will refuse to subscribe (for case 1) or will accept to subscribe (for case 2). Red bars show that the given factor decrease the chances that a person will refuse to subscribe (2) or will accept to subscribe (for case 1).

  • For case 1, we have that in 83 out of 100 cases a person having these factors will refuse to subscribe. + The largest bar is red and it refers to the duration of the last call which was less than 5.32 seconds. This factor is the main one which lowers the chance that a person will refuse to subscribe.
    • Two most important factors which increase the chance that a person will refuse to subscribe are having housing loan and unknown result of the previous campaign.
    • The other factors which weaken the chances to refuse subscription are an absence of a personal loan and a person is being divorced.
    • The other factors which improve the chances to refuse subscription are having a credit in default and a job of an admin.
  • For case 2, we have that in 51 out of 100 cases a person having these factors will accept to subscribe.
    • The largest bar is green and it refers to the duration of the last call which was less than 5.32 seconds. This factor is the main one which increses the chance that a person will subscribe.
    • Two most important factors which descrease the chance that a person will accept subscribe are having housing loan and unknown result of the previous campaign.
    • The another factor which improves the chances to accept subscription is an absence of a personal loan.
    • The other factors which weakens the chances to accept subscription are having a credit in default, being married and being younger than 48.
  • So, here is a presentation of how this factors occuring both separately and together influence the outcome: whether a person will subscribe or not.

Identifying Key Factors for Subscription

Next, let`s move from individual cases to general patterns. We are to define the most important factors which influence the result of subscription in general.

We can evaluate how significant the given variable in defining whether a person will subscribe or not.

  • The most important factor is duration of the last call.
  • Also, such factors as last contact month, poutcome - outcome of the previous marketing campaign, and housing - having personal loan, have a high influence on the outcome of the campaign.
  • It turns out that having a credit default have no influence on the outcome.
  • Moreover, we should think twice before inclusing such factors as personal education level and number of contacts performed during this campaign and for this client since they show low importance for defining whether a person will subscribe or not.

Defining How Factors Influence Each Other

Here, we will define how factors are interconnected.

We suggest to read the plot upwards for better undertanding of its logics.

  • The factor that is shown at the very bottom of the picture - response_binary - is our outcome we want to define: whether a person will subscribe or not.
  • Arrows pointing towards response_binary are key factors which have the highest influence on the outcome. 3 of them were chosen among the most important features that we defined earlier: mon - last contacted month, dur - duration of the last call, and poutcome - outcome of the previous marketing campaign. Moreover, personally we defined bal - balance level as a factor which strongly influences the outcome variable.
  • Arrows pointing towards key factors state that a feature influences a key factor.
    • For balance: the variables which can influence person`s balance level are connected either with credits or with job.
    • For poutcome: the outcome of previous campaign can be influenced by a last contact month. The logics is simple: since it has an impact on this campaign, it had an impact on the last one, too.

Next, let`s move from left to right and upwards through chains of factors for deeper exploring of interconnections between arcs.

  • Bal -> response_binary stands for an influence of a balance level on whether a person will subscribe or not (later - outcome). The higher is the balance, the higher is the chance that a person will subscribe.
  • Job -> bal stands for an influence of a type of job on a balance level. There is an arc between a job and balance since different types of jobs are differently paid.
  • Loan -> bal stands for an influence of having a personal loan on a balance level. If a person has a loan, their balance is decreased by the amount of loan.
  • Housing -> bal stands for an influence of having a housing loan on a balance level. If a person has the housing loan, their balance is decreased by the amount of loan.
  • Poutcome -> response_binary stands for an influence of outcome of the previous marketing campaign on results of this campaign. If a person agreed to subscribe the last time, the chance that he/she will agree again is increased.
  • Month -> poutcome stands for an influence of the last contact month on the results of previous campaign. It was found out that, during previous campaign, in march, september, october and december subscription rates were much higher than other months.
  • Month -> response_binary stands for an influence of the last contact month of the year on the outcome. It was found out that, during this campaign, in march, september, october and december subscription rates are much higher than during other months.
  • Dur -> response_binary stands for an influence of the last contact duration on the outcome. The longer was the last contact, the more chances that a company managed to convince a person to subscribe.

How Well Does It Work

From the point of view monetary gains and losses, working according to this model will help you to save as much money as possible. The reason is that it correctly defines non-subscribers in 99% of cases, which is even much higher than in previous model. Apply it and you will always know the customers with which it can be non-profitable to work and think, how you can change their minds in order to improve subscription rates.