Marketing analysis suite

The Suite At A Glance Extension	Primary question	Typical data	Best used for
Path to Purchase	What routes do customers take, and where do they progress or drop out?	Customer, session or journey-level steps, stages, timestamps and outcomes.	Understanding journeys, bottlenecks, common sequences and conversion paths.
Attribution Analysis	Which tracked touchpoints or channels should receive credit for a conversion or value outcome?	Customer or event-level paths containing touchpoints before a conversion or non-conversion.	Comparing channel credit, testing attribution rules and explaining touchpoint value.
Marketing Mix Modelling	How do media, controls and external factors explain outcome changes over time?	Weekly, monthly or other period-level data, optionally split by market or segment.	Estimating contribution, checking fit, exploring media carryover and supporting budget planning.

Path To Purchase

Path to Purchase is the lens through which we view the customer journey. It focuses on the sequence of steps people take before they convert, exit, or reach another outcome. It is especially useful when the analyst wants to understand the shape of customer behaviour rather than allocate media credit or estimate aggregate return.

Typical outputs include common paths, stage transitions, drop-off points, conversion routes and comparisons between successful and unsuccessful journeys. These outputs help answer practical questions such as: Which paths occur most often? Where do customers stall? Which touchpoints usually appear before conversion? Are there routes that look inefficient, repetitive or unexpectedly valuable?

Use it when the data contains ordered steps or events and the immediate objective is to improve the journey, simplify hand-offs, identify friction, or understand how people move through a funnel.

Attribution Analysis

Attribution Analysis has a focus on credit-allocation. It starts from observed touchpoints on a path and asks how much credit each touchpoint, channel or step should receive for the outcome. Different attribution rules can produce different answers, so the value of the tool is often comparative: it helps users see how sensitive channel credit is to the assumptions being used.

Typical methods include first touch, last touch, linear attribution, position-based attribution and time-decay approaches. More advanced approaches may use model-based or path-based logic to assess how removing or re-weighting a touchpoint changes the estimated path value. Note that this tool requires the use of SPSS Custom Tables.

Use it when the data contains identifiable customer or session paths and the you want to compare touchpoint credit, campaign roles, conversion assists or channel value inside tracked journeys.

Marketing Mix Modelling

Marketing Mix Modelling, often shortened to MMM, is designed for aggregate planning. It models an outcome over time using media variables, control variables, seasonality, trend and optional market or segment effects. Control variables are independent variables used to control for outcome variation that may not be caused by marketing, such as price, promotions, distribution, competitor activity, holidays, weather or economic conditions.

MMM can estimate fitted values. These are the values predicted by the model for each period. When the fitted values are decomposed, the output can show how much of the modelled outcome is associated with media, baseline or intercept, controls, market effects, trend and seasonality. It can also show diagnostics such as model fit, correlations, VIF or tolerance, residual checks, media contribution and planning screens.

Use it when you are working with time series data, and want to uncover evidence for planning decisions, such as which channels appear to contribute most, whether spend effects carry over, how markets/segments differ, or whether proposed budget moves are worth testing.

How The Tools Complement Each Other Analytical focus	What it tells you	How it supports the next tool
Path to Purchase	Shows how customers move through steps, channels and touchpoints.	Identifies the journeys and touchpoints that may deserve attribution testing.
Attribution Analysis	Shows how credit changes under different attribution assumptions.	Helps select channels or touchpoints to compare with aggregate MMM evidence.
Marketing Mix Modelling	Shows whether aggregate outcomes move with media and other drivers over time.	Checks whether journey-level and attribution findings are consistent with broader planning evidence.

For example, Path to Purchase might show that paid search often appears late in converting journeys, while social media appears earlier. Attribution Analysis can then compare how much credit each channel receives under last-touch, linear or time-decay rules. MMM can then test whether changes in paid search and social spend are associated with changes in the target outcome over time, after allowing for controls such as price, seasonality or market differences.

Choosing Where To Start

Start with the business question and the level of data available. If the question is about customer movement, start with Path to Purchase. If the question is about credit within tracked journeys, start with Attribution Analysis. If the question is about sales, revenue or volume over time, start with MMM.

A practical sequence is to use Path to Purchase to map behaviour, Attribution Analysis to compare touchpoint credit, and MMM to test whether channel patterns are visible at the business outcome level. When the tools agree, confidence usually increases. When they disagree, that is often useful too: it may reveal missing data, tracking gaps, channel overlap, market differences or a planning assumption that needs to be tested rather than accepted.