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Motimatic
Motimatic

How Motimatic Cut Forecast Variability by 90% through Cleaner Data

90%

Forecast variability reduced

100M

Rows cleaned and unified

1,000 → 5

Tables consolidated

Motimatic is a full-funnel growth engine and advertising technology platform grounded in behavioral and motivational science. Specializing in higher education and government, Motimatic helps institutions reach, engage, and motivate learners from enrollment through persistence. More than 250 colleges and universities trust Motimatic's fully managed, tech-enabled solution to improve student outcomes — all without requiring software integration or additional burden on institutional teams.

The Challenge

100M Rows, ~1,000 Tables, No Clear View of the Pipeline


Motimatic's leadership needed a clearer view of the pipeline and a better way to assess sales team performance and match reps to their best-fit prospects. The underlying data stood in the way.


Roughly 1,000 tables, 100M rows, incomplete fields with high null rates, and rich call notes and commentary sitting unstructured and under-analyzed. Real opportunities were buried inside noise, and forecasts swung widely because no one could agree on what the pipeline actually contained.


Sales leaders could not confidently rank opportunities, evaluate rep performance, or translate unstructured call context into a signal reps could act on.


a terminal showing pipeline_transform.sql

-- e:cue Data Transformation Pipeline
-- Motimatic | ~1,000 tables > 5 clean views
EXTRACT sources
FROM crm_opportunities, call_notes,
rep_activity, renewals, expansions
TRANSFORM WITH
null_rate_repair(opportunity_fields
) >> parse_unstructured_notes()
>> classify(expansion, net_new, renewal)
>> score_win_probability()
LOAD INTO unified_pipeline_view (5 tables)
>> READY FOR CUE


The Solution

From 1,000 Tables to 5 Clean, Context-Rich Views


e:cue integrated and cleaned Motimatic's sprawling data into a single operating view for real opportunities, collapsing roughly 1,000 tables into 5 clean ones, including context-rich unstructured views over call notes and commentary. Three layers did the heavy lifting:


  • Null-rate repair and opportunity cleanup : Filling incomplete fields and filtering noise so the 100M-row substrate resolved to actual, tracked opportunities.

  • Unstructured call-note analysis : Turning reps' free-text notes and commentary into structured time-bound signals Cue can read alongside quantitative fields.

  • Win-probability and rep-fit scoring : A ranked view of open opportunities by win probability, paired with a rep-performance lens for matching the right SDR to the right deal.



Cue in Action

Ranking Every Open Opportunity by Win Probability


With clean pipeline data flowing into Cue, Motimatic's sales leaders could ask natural-language questions about the pipeline and get ranked, decision-ready answers. One morning, the Sales lead asked Cue to break down June opportunities by type, then to rank them by win probability. Cue returned the segmentation, the ranked list, and offered the next relevant follow-up.


a chat box showing the following conversation: 
Motimatic: "Cue, how many opportunity do I have from 2025? Break down by type." 
Cue: "The Solution
From 1,000 Tables to 5 Clean, Context-Rich Views
e:cue integrated and cleaned Motimatic's sprawling data into a single operating view for real opportunities, collapsing roughly 1,000 tables into 5 clean ones, including context-rich unstructured views over call notes and commentary. Three layers did the heavy lifting:
Null-rate repair and opportunity cleanup : Filling incomplete fields and filtering noise so the 100M-row substrate resolved to actual, tracked opportunities.
Unstructured call-note analysis : Turning reps' free-text notes and commentary into structured time-bound signals Cue can read alongside quantitative fields.
Win-probability and rep-fit scoring : A ranked view of open opportunities by win probability, paired with a rep-performance lens for matching the right SDR to the right deal.
Cue in Action
Ranking Every Open Opportunity by Win Probability
With clean pipeline data flowing into Cue, Motimatic's sales leaders could ask natural-language questions about the pipeline and get ranked, decision-ready answers. One morning, the Sales lead asked Cue to break down June opportunities by type, then to rank them by win probability. Cue returned the segmentation, the ranked list, and offered the next relevant follow-up." 
Motimatic: "@Cue rank these opportunities by win probability" 
Cue: Ranked view of the 37 June 2025 opportunities.
Highlighting the top 5 win probabilities — the remaining
27 sit at 45% or lower.
96.53% Opportunity #1 (Top
rank)
93.68% Opportunity #2 (July
2025)
88.63% Opportunity #3
86.92% Opportunity #4
(Renewal)
84.80% Opportunity #5
(2025/2026)

The Results

A Pipeline Leaders Can Actually Run On

With unified data and Cue's conversational analytics, Motimatic shifted from guessing at pipeline quality to running sales off a single, ranked view.

Time-Bound Signals

Improved time-bound interaction signals surfaced from previously unstructured call notes

Ranked Pipeline

Every open opportunity ranked by win probability, updated as new signals arrive

SDR Fit

New sales-rep performance view for matching opportunities to the right SDR

90%

Reduction in forecast variability, giving leadership a pipeline number they can defend

Company
 

Motimatic

Industry
 

Media

Use Case
 

LTV, Forecasting, Sales Productivity

Data Complexity

Medium

Get Started

Ready to enable data-driven marketing?

See how e:cue can transform fragmented marketing data into a single source of truth that drives real enrollment outcomes.

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