how it works new

From our extensive research and experience in capturing expert logic across various industries and applications, we have identified a number of fundamental logic forms or types that you may require to build any Virtual Advisor you wish. And what is more, CLEVVA allows you to blend logic types within VAs so that you can reflect the relevant decision flow required to guide the decisions and actions without being bound by one approach.


This allows you to apply filters to narrow down data and to find matching records based on a series of conditions. These conditions can be built up using sequential logic that determines the users requirements within a specific order of choices; or they can be inferred based on a previous selection, allowing for a more dynamic, flexible decision flow.

To explain how this works, see the data sample shown on the right. Imagine this was a sample of thousands of vehicle records uploaded into CLEVVA. Each vehicle record has a set of attributes or factors that relate to that record. For example the make, the year, the transmission, the number of doors, the price etc.

Using filtering logic, you can enable the user to specify the factor sets that will drive their decision on vehicle, and as they make these choices, CLEVVA works out the matching records.

One way to do this is to specify an order of questions. This could mean starting with the question “Do you have a preferred make of vehicle?”, followed by “Would you prefer an automatic or manual transmission?” and so on. As the user makes the selection, CLEVVA can work out matching records and show these at any pre-specified point e.g. once you are down to 10 matching records.

Alternatively you can ask CLEVVA to work out the questions, based off the factor sets within the records. You can set different question orders, weightings and wording (as required) and then the data logic takes over. You can also set questions to auto-answer if the answers are known e.g. via the customer entity record.

Based on the user priorities, CLEVVA automatically works out the remaining differentiating factors and only asks these. This means CLEVVA can adapt to any route that the user wishes to take to find their preferred vehicle. And as data records are updated, the questions update automatically off the factor records.

An illustration of how filtered logic can be applied:

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An example of applying filter conditions:

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–  Need Identification

–  Resource Identification

–  Solution/Product Finding


Within data set, factors, reference content, entities and users, you may at any point wish to apply rules or logic to a set of these (without having to apply them to every item). CLEVVA allows you to very easily apply logic that can be inherited by items within lower related folders. This from a logic maintenance perspective is very important, as you may wish to suddenly apply a special offer trigger to a specific category of vehicles for one day only, and using hierarchical logic you can set that trigger at the relevant data level and all the records within this data folder (and below) will automatically have this logic applied. The same goes for linking a specific brochure or video clip to a set of records, or linking a VA to a set of factors. With hierarchical logic, this laborious action can be achieved within seconds (and then changed in seconds).

An example of hierarchical logic being applied to data:



              >A series




This form of logic is typically not reflected in any documentation, as it relates to how experts make ‘intuitive’ decisions simply by analysing related factors before making a specific decision. With cascading logic, relationships between factors can be mapped at various levels to help a user answer a specific question.

Take a ‘simple’ prioritising decision as an example (see right). To decide if a call is high, normal or low priority, I may need to consider the financial impact, the customer impact and the safety factor. The problem is that to determine the financial impact, I also need to determine whether the issue could result in a loss of sales and if the cost to repair the issue will increase if left for more than 8 hours. And often to determine if it will result in a loss of sales, one needs to consider other factors. And so it cascades down.

With CLEVVA, you can link cascading logic at factor level to offer users, if required, diagnostic support on all related factors that then offers the user a decision recommendation. This is very useful when context matters, and when users are not aware of all related factors they must consider.

An example of cascading logic being applied at level 1:

Screen Shot 2016-07-12 at 1.08.51 PM

An example of cascading logic being applied at level 2:

Screen Shot 2016-07-12 at 1.08.56 PM


–  Need Identification

–  Resource Identification

–  Solution/Product Finding


This allows you to create logical relationships between data records to ensure that appropriate related advice can be offered. It is typically used to support decision making where the user is not aware of relevant and related issues or factors that they should consider.

They simply want an answer to the problem they think they have. By identifying unexpressed or unidentified topics for consideration, you can expose the user to a wider set of considerations.

This type of logic is typically used for:

–  Cross Selling

–  Upselling

–  Offering Warnings

–  Related Information


At a vehicle dealership a sales person may be having a conversation with a customer where they may indicate that they need the vehicle to be able to tow a caravan. This not only informs the decisions about the power of the engine required, but it also suggests that an appropriate related conversation about purchasing and installing a tow bar with the vehicle should be had.   


A client may express the financial need to have their child’s school fees funded in the short term, but may not have considered the importance of also putting in a plan to save for future education expenses (to avoid them landing up in this short term cash flow problem again).


This type of logic is used when a decision needs to be made by applying a calculation to a number of selected factors – effectively assigning a numeric to each selection and then based on the total across selection, offering the user a specific answer that matches the total range.

To support this, CLEVVA allows you to apply multiple numeric values to specific factors so that a calculation can be done and a decision made based on a number of ranges against a specific answer.

To explain this using an example, imagine you wished to provide a client advice on whether to choose an automatic or manual transmission for their new car. And in making this decision, a weighted average across a series of questions is proposed (by your internal experts). See below an example of the Transmission Score Table your experts may use to score the client, and a Transmission Calculation Table they would use to calculate if they would recommend a manual or automatic transmission.


A Transmission Score Table:

Screen Shot 2016-07-12 at 1.11.59 PM


Lets assume that this is the Transmission Calculation Table That We Should Reference:

Screen Shot 2016-07-12 at 1.12.18 PM

And lets assume that this is the Transmission Score that a certain client receives (based on the table to the left)

Screen Shot 2016-07-12 at 1.13.42 PM


Given the Total Transmission Score is AND that it is TRUE that 1 is >= 0 and < 3 (see the Transmission Calculation Table above)

The recommended vehicle transmission that should be offered to this client would be ‘Manual’


– Score Based Decisions

– Weight Based Decisions


Triggered logic allows for specific conditions to be defined that fire off specific actions. This can be used to initiate a related conversation, to warn users about something that may be relevant or to manage the workflow of the interaction e.g. send a linked VA to a specified person.

In short, CLEVVA allows you to set conditions at a global level or at a specific VA or even at a step level – conditions that if supported need to trigger a pre-defined action for the user. This logic is very useful in driving rule-based actions on any level or interaction with a user.

This type of logic is typically used for:

–  Cross Selling

–  Lead Generation

–  Offering Warnings

The levels you can set your triggers at include:

Screen Shot 2016-07-12 at 1.15.13 PM


During the sales conversation an offer of an extended warranty could be set to trigger if the following predefined conditions are both met:

1. The vehicle they are looking to purchase has less than 12 months left on the existing warranty

2. The customer indicated previously that they have budget constraints

Note: You can have both/and, either/or, and else settings that relate to multiple criteria within any trigger. This offers you amazing flexibility and power within your VA logic.


In certain cases, you need to speed things up by assuming certain facts or outcomes based on what you already know i.e. answers to questions can be inferred from previous selections or decisions made. Experts do this all the time – they may ask you 2 or 3 questions and then jump to a series of conclusions simply because these questions tell them all they need to know to be confident that a number of inferred or assumed facts are true.

This type of logic is commonly used in sales where experienced sales professionals have seen the patterns and can infer a whole lot from just a few questions.

For example, imagine that a customer is on the Bronze Extended Motor Warranty Plus plan (see right for the data table). Just from this fact, an expert can safely infer that they are NOT covered for Repair Minor Body Paint Scratchesand you should therefore talk to them about getting this covered.

This means that all CLEVVA needs to know is which plan the customer is on and the factors can be maintained by the data. If the data is maintained within a 3rd party rules engine, it can be kept updated in CLEVVA via an integration. This ensures that when these factor rules change, the logic in CLEVVA remains valid (being entirely driven off data).

Note: In this case, using inherited logic, CLEVVA would also know that the Silver Extended Motor Warranty and Silver Extended Motor Warranty Plus both exclude Repair Minor Body Part Scratches because they belong to the Silver Category.

The example of inferred logic being applied

A customer has the following extended motor warranty plan:

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The Extended Motor Warranty Product Category:

Screen Shot 2016-07-12 at 1.16.40 PM


–  Sales (driving relevant customer conversations)

–  Service (driving relevant customer conversations)

–  Technical (driving speed to diagnostic


This is the most commonly used, and least preferred form of logic, simply due to its limitation in reflecting the multi-dimensional nature of true decision-making. Unfortunately due to the the extensive use of process flows, this form of logic is typically used as a starting point for many VA builds.

There are many inherent dangers in using sequential logic to direct decision logic, primarily because it is difficult to maintain and almost impossible to work out all the possible end scenarios. However, sequential logic does have its place, especially when mapping out steps or decisions that are required to happen in a given sequence (for whatever reason).

So while we strongly suggest that when converting standard operating procedure logic into VAs, you do not simply capture the logic sequentially (but rather interrogate the purpose of the decision and leverage alternative logic forms), there will be elements within the decision flow that do require forced sequencing and it is here where this form becomes valuable.

To illustrate sequential logic and how it can be captured, consider the process defined for a line manager who needs to make a hiring decision based on a prescribed procedure (see right). With CLEVVA, you can ensure that based on the user choices, certain support or triggers can be set to guide them through the procedure effortlessly (see below).

Screen Shot 2016-07-12 at 1.18.40 PM

Procedure for hiring a new resource


A key benefit of using CLEVVA to replicate sequential logic is that it offers you a record of evidence that the user did in fact follow these steps. This is very useful for compliance purposes.


–  Trouble Shooting

–  Standard Operating Procedures

–  Activity Guides

– Surveys